var bibbase_data = {"data":"<img src=\"//bibbase.org/img/ajax-loader.gif\" id=\"spinner\"\n  style=\"display: none;\" alt=\"Loading..\" />\n\n<div id=\"bibbase\">\n\n  <script type=\"text/javascript\">\n    var bibbase = {\n      params: {\"bib\":\"www.haz.ca/publications.bib\",\"groupby\":\"year\",\"jsonp\":\"1\",\"token\":\"51d12e41c2125f923b518bf3311b9ea2\",\"host\":\"bibbase.org\"},\n      data: [{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"title\":\"Planning as a Service\",\"author\":[{\"firstnames\":[\"Yi\"],\"propositions\":[],\"lastnames\":[\"Ding\"],\"suffixes\":[]},{\"firstnames\":[\"Cam\"],\"propositions\":[],\"lastnames\":[\"Cunningham\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Nir\"],\"propositions\":[],\"lastnames\":[\"Lipovetzky\"],\"suffixes\":[]}],\"booktitle\":\"International Conference on Automated Planning and Scheduling: System Demonstrations\",\"year\":\"2023\",\"url_paper\":\"https://icaps23.icaps-conference.org/demos/papers/8837_paper.pdf\",\"abstract\":\"Planning as a service (PaaS) provides an extendable API to deploy planners online in local or cloud servers. The service provides a queue manager to control a set of workers, which can easily be extended with one of several planners available in PLANUTILS. PaaS is designed to overcome the limitations of the existing online solver.planning.domains interface and widen the adoption of planning technology in education, research, and industry.\",\"bibtex\":\"@incollection{ding-icapsdemo-2023,\\n  title     = {Planning as a Service},\\n  author    = {Yi Ding and Cam Cunningham and Christian Muise and Nir Lipovetzky},\\n  booktitle = {International Conference on Automated Planning and Scheduling: System Demonstrations},\\n  year      = {2023},\\n  url_paper = {https://icaps23.icaps-conference.org/demos/papers/8837_paper.pdf},\\n  abstract  = {Planning as a service (PaaS) provides an extendable API to deploy planners online in local or cloud servers. The service provides a queue manager to control a set of workers, which can easily be extended with one of several planners available in PLANUTILS. PaaS is designed to overcome the limitations of the existing online solver.planning.domains interface and widen the adoption of planning technology in education, research, and industry.}\\n}\\n\\n\",\"author_short\":[\"Ding, Y.\",\"Cunningham, C.\",\"Muise, C.\",\"Lipovetzky, N.\"],\"key\":\"ding-icapsdemo-2023\",\"id\":\"ding-icapsdemo-2023\",\"bibbaseid\":\"ding-cunningham-muise-lipovetzky-planningasaservice-2023\",\"role\":\"author\",\"urls\":{\" paper\":\"https://icaps23.icaps-conference.org/demos/papers/8837_paper.pdf\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":5,\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"title\":\"Plan4Dial: A Dialogue Planning Framework\",\"author\":[{\"firstnames\":[\"Rebecca\",\"De\"],\"propositions\":[],\"lastnames\":[\"Venezia\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]}],\"booktitle\":\"International Conference on Automated Planning and Scheduling: System Demonstrations\",\"year\":\"2023\",\"url_paper\":\"https://icaps23.icaps-conference.org/demos/papers/8139_paper.pdf\",\"abstract\":\"Dialogue agents have exploded in importance in recent years as businesses increasingly use chat-bots to serve their customer base. However, many of these dialogue systems rely on black-box language models that cannot be verified for predictability, making them a liability. One proposed solution is dialogue planning, which uses planning to generate a complete dialogue tree and allows for the verification of the agent’s actions. Despite the plethora of existing research in this space, there is no open and readily available modern framework for dialogue planning development. We propose Plan4Dial, an open-source system for creating dialogue planning chat-bots. Plan4Dial allows developers to declare complex chat-bots with ease by writing an intuitive YAML specification which the system converts to raw PDDL. Plan4Dial then calls a state-of-the-art planner to generate a dialogue tree which we execute with an extension of IBM’s dialogue plan executor, Hovor. We also created WIDGET, an embeddable web user interface for users to chat with their agents. Our work allows for the simple creation of complex but verifiable goal-oriented dialogue agents using planning technology.\",\"bibtex\":\"@incollection{devenezia-icapsdemo-2023,\\n  title     = {Plan4Dial: A Dialogue Planning Framework},\\n  author    = {Rebecca De Venezia and Christian Muise},\\n  booktitle = {International Conference on Automated Planning and Scheduling: System Demonstrations},\\n  year      = {2023},\\n  url_paper = {https://icaps23.icaps-conference.org/demos/papers/8139_paper.pdf},\\n  abstract  = {Dialogue agents have exploded in importance in recent years as businesses increasingly use chat-bots to serve their customer base. However, many of these dialogue systems rely on black-box language models that cannot be verified for predictability, making them a liability. One proposed solution is dialogue planning, which uses planning to generate a complete dialogue tree and allows for the verification of the agent’s actions. Despite the plethora of existing research in this space, there is no open and readily available modern framework for dialogue planning development. We propose Plan4Dial, an open-source system for creating dialogue planning chat-bots. Plan4Dial allows developers to declare complex chat-bots with ease by writing an intuitive YAML specification which the system converts to raw PDDL. Plan4Dial then calls a state-of-the-art planner to generate a dialogue tree which we execute with an extension of IBM’s dialogue plan executor, Hovor. We also created WIDGET, an embeddable web user interface for users to chat with their agents. Our work allows for the simple creation of complex but verifiable goal-oriented dialogue agents using planning technology.}\\n}\\n\\n\",\"author_short\":[\"Venezia, R. D.\",\"Muise, C.\"],\"key\":\"devenezia-icapsdemo-2023\",\"id\":\"devenezia-icapsdemo-2023\",\"bibbaseid\":\"venezia-muise-plan4dialadialogueplanningframework-2023\",\"role\":\"author\",\"urls\":{\" paper\":\"https://icaps23.icaps-conference.org/demos/papers/8139_paper.pdf\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"title\":\"Learning to Recognize Reachable States from Visual Domains\",\"author\":[{\"firstnames\":[\"Ella\"],\"propositions\":[],\"lastnames\":[\"Morgan\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]}],\"booktitle\":\"Canadian Conference on Artificial Intelligence\",\"year\":\"2023\",\"url_paper\":\"https://caiac.pubpub.org/pub/0ggeub8s/release/1\",\"abstract\":\"While planning models are symbolic and precise, the real world is noisy and unstructured. This work aims to bridge the gap between noise and structure by aligning visualizations of planning states to the underlying state space structure. Further, we do so in the presence of noise and augmentations that simulates a commonly overlooked property of real environments: several variations of semantically equivalent states. First, we create a dataset that visualizes states for several common planning domains; each state is generated in a way that introduces variability or noise. E.g., objects changing in location or appearance in a manner that preserves semantic meaning. First we train a contrastive learning model to predict the underlying states from the images. We then evaluate how we can align the predictions of a given sequence of visualized states with the problem’s reachable state space, taking advantage of the known structure to improve predictions. We compare two methods for doing so: a greedy approach and Viterbi’s algorithm, a well-established algorithm for observation decoding given a hidden Markov model. The results demonstrate that these alignment methods can correct errors in the model and significantly improve predictive accuracy.\",\"bibtex\":\"@incollection{morgan-cai-2023,\\n  title     = {Learning to Recognize Reachable States from Visual Domains},\\n  author    = {Ella Morgan and Christian Muise},\\n  booktitle = {Canadian Conference on Artificial Intelligence},\\n  year      = {2023},\\n  url_paper = {https://caiac.pubpub.org/pub/0ggeub8s/release/1},\\n  abstract  = {While planning models are symbolic and precise, the real world is noisy and unstructured. This work aims to bridge the gap between noise and structure by aligning visualizations of planning states to the underlying state space structure. Further, we do so in the presence of noise and augmentations that simulates a commonly overlooked property of real environments: several variations of semantically equivalent states. First, we create a dataset that visualizes states for several common planning domains; each state is generated in a way that introduces variability or noise. E.g., objects changing in location or appearance in a manner that preserves semantic meaning. First we train a contrastive learning model to predict the underlying states from the images. We then evaluate how we can align the predictions of a given sequence of visualized states with the problem’s reachable state space, taking advantage of the known structure to improve predictions. We compare two methods for doing so: a greedy approach and Viterbi’s algorithm, a well-established algorithm for observation decoding given a hidden Markov model. The results demonstrate that these alignment methods can correct errors in the model and significantly improve predictive accuracy.}\\n}\\n\\n\",\"author_short\":[\"Morgan, E.\",\"Muise, C.\"],\"key\":\"morgan-cai-2023\",\"id\":\"morgan-cai-2023\",\"bibbaseid\":\"morgan-muise-learningtorecognizereachablestatesfromvisualdomains-2023\",\"role\":\"author\",\"urls\":{\" paper\":\"https://caiac.pubpub.org/pub/0ggeub8s/release/1\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"title\":\"The Generalizability of FOND Solutions in Uncertain Environments\",\"author\":[{\"firstnames\":[\"Victoria\"],\"propositions\":[],\"lastnames\":[\"Armstrong\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]}],\"booktitle\":\"Workshop on Integrated Acting, Planning and Execution\",\"year\":\"2023\",\"url_paper\":\"https://mulab.ai/papers/2023-intex-armstrong.pdf\",\"abstract\":\"Logical regression has proven to be a powerful mechanism for computing compact solutions to non-deterministic planning problems. However, the impact on the generality of the generated solutions has thus far been largely unstudied. We analyze the compact solutions produced by a leading FOND planner, PRP, and develop a logical encoding that represents all possible states that the policy can handle. Through the use of a #-SAT solver, we count the number of models that satisfy the logical encoding (corresponding precisely to the states the policy is able to handle). We analyze the solution representation on seven standard FOND benchmarks and compare the generality of these policies to the reachable state space of the policy applied to the problem’s initial state. Our work can be seen as a generalization of similar studies for deterministic planning domains and clearly demonstrates the broad generalizability of these compact representations.\",\"bibtex\":\"@incollection{armstrong-intex-2023,\\n  title     = {The Generalizability of FOND Solutions in Uncertain Environments},\\n  author    = {Victoria Armstrong and Christian Muise},\\n  booktitle = {Workshop on Integrated Acting, Planning and Execution},\\n  year      = {2023},\\n  url_paper = {https://mulab.ai/papers/2023-intex-armstrong.pdf},\\n  abstract  = {Logical regression has proven to be a powerful mechanism for computing compact solutions to non-deterministic planning problems. However, the impact on the generality of the generated solutions has thus far been largely unstudied. We analyze the compact solutions produced by a leading FOND planner, PRP, and develop a logical encoding that represents all possible states that the policy can handle. Through the use of a #-SAT solver, we count the number of models that satisfy the logical encoding (corresponding precisely to the states the policy is able to handle). We analyze the solution representation on seven standard FOND benchmarks and compare the generality of these policies to the reachable state space of the policy applied to the problem’s initial state. Our work can be seen as a generalization of similar studies for deterministic planning domains and clearly demonstrates the broad generalizability of these compact representations.}\\n}\\n\\n\",\"author_short\":[\"Armstrong, V.\",\"Muise, C.\"],\"key\":\"armstrong-intex-2023\",\"id\":\"armstrong-intex-2023\",\"bibbaseid\":\"armstrong-muise-thegeneralizabilityoffondsolutionsinuncertainenvironments-2023\",\"role\":\"author\",\"urls\":{\" paper\":\"https://mulab.ai/papers/2023-intex-armstrong.pdf\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"title\":\"From State Spaces to Semigroups: Leveraging Algebraic Formalism for Automated Planning\",\"author\":[{\"firstnames\":[\"Alice\"],\"propositions\":[],\"lastnames\":[\"Petrov\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]}],\"booktitle\":\"Workshop on Heuristics and Search for Domain-independent Planning\",\"year\":\"2023\",\"url_paper\":\"https://openreview.net/pdf?id=ocTyIiCroh\",\"abstract\":\"This paper introduces an algebraic formalism linking transformation semigroups and the state transition systems induced by classical planning problems. We investigate some basic planning problems with interesting properties and establish fundamental characteristics of the corresponding semigroups, such as their ideals and Green’s relations. Furthermore, we leverage semigroup theory to propose new approaches to existing concepts in automated planning, including the identification of landmark actions and the study of dead ends. We demonstrate that algebraic results can be applied to facilitate an understanding of a planning problem’s state space and explore its solutions, thus verifying the relevance and effectiveness of such formal modeling.\",\"bibtex\":\"@incollection{petrov-hsdip-2023,\\n  title     = {From State Spaces to Semigroups: Leveraging Algebraic Formalism for Automated Planning},\\n  author    = {Alice Petrov and Christian Muise},\\n  booktitle = {Workshop on Heuristics and Search for Domain-independent Planning},\\n  year      = {2023},\\n  url_paper = {https://openreview.net/pdf?id=ocTyIiCroh},\\n  abstract  = {This paper introduces an algebraic formalism linking transformation semigroups and the state transition systems induced by classical planning problems. We investigate some basic planning problems with interesting properties and establish fundamental characteristics of the corresponding semigroups, such as their ideals and Green’s relations. Furthermore, we leverage semigroup theory to propose new approaches to existing concepts in automated planning, including the identification of landmark actions and the study of dead ends. We demonstrate that algebraic results can be applied to facilitate an understanding of a planning problem’s state space and explore its solutions, thus verifying the relevance and effectiveness of such formal modeling.}\\n}\\n\\n\",\"author_short\":[\"Petrov, A.\",\"Muise, C.\"],\"key\":\"petrov-hsdip-2023\",\"id\":\"petrov-hsdip-2023\",\"bibbaseid\":\"petrov-muise-fromstatespacestosemigroupsleveragingalgebraicformalismforautomatedplanning-2023\",\"role\":\"author\",\"urls\":{\" paper\":\"https://openreview.net/pdf?id=ocTyIiCroh\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"title\":\"PARIS: Planning Algorithms for Reconfiguring Independent Sets\",\"author\":[{\"firstnames\":[\"Remo\"],\"propositions\":[],\"lastnames\":[\"Christen\"],\"suffixes\":[]},{\"firstnames\":[\"Salome\"],\"propositions\":[],\"lastnames\":[\"Eriksson\"],\"suffixes\":[]},{\"firstnames\":[\"Michael\"],\"propositions\":[],\"lastnames\":[\"Katz\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Alice\"],\"propositions\":[],\"lastnames\":[\"Petrov\"],\"suffixes\":[]},{\"firstnames\":[\"Florian\"],\"propositions\":[],\"lastnames\":[\"Pommerening\"],\"suffixes\":[]},{\"firstnames\":[\"Jendrik\"],\"propositions\":[],\"lastnames\":[\"Seipp\"],\"suffixes\":[]},{\"firstnames\":[\"Silvan\"],\"propositions\":[],\"lastnames\":[\"Sievers\"],\"suffixes\":[]},{\"firstnames\":[\"David\"],\"propositions\":[],\"lastnames\":[\"Speck\"],\"suffixes\":[]}],\"booktitle\":\"26th European Conference on Artificial Intelligence\",\"year\":\"2023\",\"abstract\":\"Combinatorial reconfiguration studies how one solution of a combinatorial problem can be transformed into another. The transformation can only make small local changes and may not leave the solution space. An important example is the independent set reconfiguration (ISR) problem, where an independent set of a graph (a subset of its vertices without edges between them) has to be transformed into another one by a sequence of modifications that remove a vertex or add another that is not adjacent to any vertex in the set. The 1st Combinatorial Reconfiguration Challenge (CoRe Challenge 2022) was a competition focused on the ISR problem. The PARIS team participated with two solvers that model the ISR problem as a planning problem and employ different planning techniques for solving it. The solvers successfully competed in the challenge and were awarded 4 first, 3 second, and 3 third places across 9 tracks. In this work, we show how to model ISR problems as planning tasks and describe the planning techniques used by these solvers. For a fair comparison to competing ISR approaches, we re-run the entire competition under equal computational conditions. Besides showcasing the success of planning technology, we hope that this work will create a cross-fertilization of the two research fields.\",\"url_paper\":\"https://ebooks.iospress.nl/doi/10.3233/FAIA230303\",\"bibtex\":\"@incollection{christen-ecai-2023,\\n  title     = {PARIS: Planning Algorithms for Reconfiguring Independent Sets},\\n  author    = {Remo Christen and Salome Eriksson and Michael Katz and Christian Muise and Alice Petrov and Florian Pommerening and Jendrik Seipp and Silvan Sievers and David Speck},\\n  booktitle = {26th European Conference on Artificial Intelligence},\\n  year      = {2023},\\n  abstract  = {Combinatorial reconfiguration studies how one solution of a combinatorial problem can be transformed into another. The transformation can only make small local changes and may not leave the solution space. An important example is the independent set reconfiguration (ISR) problem, where an independent set of a graph (a subset of its vertices without edges between them) has to be transformed into another one by a sequence of modifications that remove a vertex or add another that is not adjacent to any vertex in the set. The 1st Combinatorial Reconfiguration Challenge (CoRe Challenge 2022) was a competition focused on the ISR problem. The PARIS team participated with two solvers that model the ISR problem as a planning problem and employ different planning techniques for solving it. The solvers successfully competed in the challenge and were awarded 4 first, 3 second, and 3 third places across 9 tracks. In this work, we show how to model ISR problems as planning tasks and describe the planning techniques used by these solvers. For a fair comparison to competing ISR approaches, we re-run the entire competition under equal computational conditions. Besides showcasing the success of planning technology, we hope that this work will create a cross-fertilization of the two research fields.},\\n  url_paper = {https://ebooks.iospress.nl/doi/10.3233/FAIA230303}\\n}\\n\\n\",\"author_short\":[\"Christen, R.\",\"Eriksson, S.\",\"Katz, M.\",\"Muise, C.\",\"Petrov, A.\",\"Pommerening, F.\",\"Seipp, J.\",\"Sievers, S.\",\"Speck, D.\"],\"key\":\"christen-ecai-2023\",\"id\":\"christen-ecai-2023\",\"bibbaseid\":\"christen-eriksson-katz-muise-petrov-pommerening-seipp-sievers-etal-parisplanningalgorithmsforreconfiguringindependentsets-2023\",\"role\":\"author\",\"urls\":{\" paper\":\"https://ebooks.iospress.nl/doi/10.3233/FAIA230303\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":4,\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"title\":\"Planning for Proofs\",\"author\":[{\"firstnames\":[\"Alice\"],\"propositions\":[],\"lastnames\":[\"Petrov\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]}],\"booktitle\":\"Scheduling and Planning Applications woRKshop\",\"year\":\"2023\",\"abstract\":\"This paper explores the application of automated planning to automated theorem proving, which is a branch of automated reasoning concerned with the development of algorithms and computer programs to construct mathematical proofs. In particular, we investigate the use of planning to construct elementary proofs in abstract algebra, which provides a rigorous and axiomatic framework for studying algebraic structures such as groups, rings, fields, and modules. We implement basic implications, equalities, and rules in both deterministic and non-deterministic domains to model commutative rings and deduce elementary results about them. The success of this initial implementation suggests that the well-established techniques seen in automated planning are applicable to the relatively newer field of automated theorem proving. Likewise, automated theorem proving provides a new, challenging domain for automated planning.\",\"bibtex\":\"@incollection{petrov-spark-2023,\\n  title     = {Planning for Proofs},\\n  author    = {Alice Petrov and Christian Muise},\\n  booktitle = {Scheduling and Planning Applications woRKshop},\\n  year      = {2023},\\n  abstract  = {This paper explores the application of automated planning to automated theorem proving, which is a branch of automated reasoning concerned with the development of algorithms and computer programs to construct mathematical proofs. In particular, we investigate the use of planning to construct elementary proofs in abstract algebra, which provides a rigorous and axiomatic framework for studying algebraic structures such as groups, rings, fields, and modules. We implement basic implications, equalities, and rules in both deterministic and non-deterministic domains to model commutative rings and deduce elementary results about them. The success of this initial implementation suggests that the well-established techniques seen in automated planning are applicable to the relatively newer field of automated theorem proving. Likewise, automated theorem proving provides a new, challenging domain for automated planning.}\\n}\\n\\n\",\"author_short\":[\"Petrov, A.\",\"Muise, C.\"],\"key\":\"petrov-spark-2023\",\"id\":\"petrov-spark-2023\",\"bibbaseid\":\"petrov-muise-planningforproofs-2023\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"title\":\"Towards Human-Aware AI via Planning with Epistemic Preferences\",\"author\":[{\"firstnames\":[\"Toryn\",\"Q.\"],\"propositions\":[],\"lastnames\":[\"Klassen\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Sheila\",\"A.\"],\"propositions\":[],\"lastnames\":[\"McIlraith\"],\"suffixes\":[]}],\"booktitle\":\"International Workshop on Human-Aware and Explainable Planning\",\"year\":\"2023\",\"url_paper\":\"https://openreview.net/pdf?id=nkn1rcI6wd\",\"abstract\":\"Within the field of automated planning, two areas of study are planning with preferences, and epistemic planning. Planning with preferences involves generating plans that optimize for properties of the plan instead of, or in addition to, trying to reach a fixed goal. Epistemic planning allows for planning over the knowledge or belief states of one or more agents for the purpose of achieving epistemic goals (where agents have particular states of knowledge or belief). In this paper we motivate and explore the task of planning with epistemic preferences, proposing a method by which existing automated planning techniques can be combined for this purpose. Epistemic preferences may better allow for representing what humans want, and have benefits for AI safety.\",\"bibtex\":\"@incollection{klassen-haxp-2023,\\n  title     = {Towards Human-Aware AI via Planning with Epistemic Preferences},\\n  author    = {Toryn Q. Klassen and Christian Muise and Sheila A. McIlraith},\\n  booktitle = {International Workshop on Human-Aware and Explainable Planning},\\n  year      = {2023},\\n  url_paper = {https://openreview.net/pdf?id=nkn1rcI6wd},\\n  abstract  = {Within the field of automated planning, two areas of study are planning with preferences, and epistemic planning. Planning with preferences involves generating plans that optimize for properties of the plan instead of, or in addition to, trying to reach a fixed goal. Epistemic planning allows for planning over the knowledge or belief states of one or more agents for the purpose of achieving epistemic goals (where agents have particular states of knowledge or belief). In this paper we motivate and explore the task of planning with epistemic preferences, proposing a method by which existing automated planning techniques can be combined for this purpose. Epistemic preferences may better allow for representing what humans want, and have benefits for AI safety.}\\n}\\n\\n\",\"author_short\":[\"Klassen, T. Q.\",\"Muise, C.\",\"McIlraith, S. A.\"],\"key\":\"klassen-haxp-2023\",\"id\":\"klassen-haxp-2023\",\"bibbaseid\":\"klassen-muise-mcilraith-towardshumanawareaiviaplanningwithepistemicpreferences-2023\",\"role\":\"author\",\"urls\":{\" paper\":\"https://openreview.net/pdf?id=nkn1rcI6wd\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"title\":\"Planning with Epistemic Preferences\",\"author\":[{\"firstnames\":[\"Toryn\",\"Q.\"],\"propositions\":[],\"lastnames\":[\"Klassen\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Sheila\",\"A.\"],\"propositions\":[],\"lastnames\":[\"McIlraith\"],\"suffixes\":[]}],\"booktitle\":\"International Conference on Principles of Knowledge Representation and Reasoning\",\"pages\":\"752–756\",\"year\":\"2023\",\"url_paper\":\"https://proceedings.kr.org/2023/76/kr2023-0076-klassen-et-al.pdf\",\"abstract\":\"Within the field of automated planning, two areas of study are planning with preferences and epistemic planning. Planning with preferences involves generating plans that optimize for properties of the plan instead of, or in addition to, trying to reach a fixed goal. Epistemic planning allows for planning over the knowledge or belief states of one or more agents for the purpose of achieving epistemic goals (where agents have particular states of knowledge or belief). In this paper we motivate and explore the task of planning with epistemic preferences, proposing a method by which existing automated planning techniques can be combined for this purpose.\",\"bibtex\":\"@incollection{klassen-kr-2023,\\n  title     = {Planning with Epistemic Preferences},\\n  author    = {Toryn Q. Klassen and Christian Muise and Sheila A. McIlraith},\\n  booktitle = {International Conference on Principles of Knowledge Representation and Reasoning},\\n  pages     = {752--756},\\n  year      = {2023},\\n  url_paper = {https://proceedings.kr.org/2023/76/kr2023-0076-klassen-et-al.pdf},\\n  abstract  = {Within the field of automated planning, two areas of study are planning with preferences and epistemic planning. Planning with preferences involves generating plans that optimize for properties of the plan instead of, or in addition to, trying to reach a fixed goal. Epistemic planning allows for planning over the knowledge or belief states of one or more agents for the purpose of achieving epistemic goals (where agents have particular states of knowledge or belief). In this paper we motivate and explore the task of planning with epistemic preferences, proposing a method by which existing automated planning techniques can be combined for this purpose.}\\n}\\n\\n\",\"author_short\":[\"Klassen, T. Q.\",\"Muise, C.\",\"McIlraith, S. A.\"],\"key\":\"klassen-kr-2023\",\"id\":\"klassen-kr-2023\",\"bibbaseid\":\"klassen-muise-mcilraith-planningwithepistemicpreferences-2023\",\"role\":\"author\",\"urls\":{\" paper\":\"https://proceedings.kr.org/2023/76/kr2023-0076-klassen-et-al.pdf\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":6,\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"title\":\"TOBY: A Tool for Exploring Data in Academic Survey Papers\",\"author\":[{\"firstnames\":[\"Tathagata\"],\"propositions\":[],\"lastnames\":[\"Chakraborti\"],\"suffixes\":[]},{\"firstnames\":[\"Jungkoo\"],\"propositions\":[],\"lastnames\":[\"Kang\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Sarath\"],\"propositions\":[],\"lastnames\":[\"Sreedharan\"],\"suffixes\":[]},{\"firstnames\":[\"Michael\"],\"propositions\":[],\"lastnames\":[\"Walker\"],\"suffixes\":[]},{\"firstnames\":[\"Daniel\"],\"propositions\":[],\"lastnames\":[\"Szafir\"],\"suffixes\":[]},{\"firstnames\":[\"Tom\"],\"propositions\":[],\"lastnames\":[\"Williams\"],\"suffixes\":[]}],\"booktitle\":\"arXiv\",\"year\":\"2023\",\"url_paper\":\"https://arxiv.org/abs/2306.10051\",\"abstract\":\"This paper describes TOBY, a visualization tool that helps a user explore the contents of an academic survey paper. The visualization consists of four components: a hierarchical view of taxonomic data in the survey, a document similarity view in the space of taxonomic classes, a network view of citations, and a new paper recommendation tool. In this paper, we will discuss these features in the context of three separate deployments of the tool.\",\"bibtex\":\"@incollection{chakraborti-arxiv-2023,\\n  title     = {TOBY: A Tool for Exploring Data in Academic Survey Papers},\\n  author    = {Tathagata Chakraborti and Jungkoo Kang and Christian Muise and Sarath Sreedharan and Michael Walker and Daniel Szafir and Tom Williams},\\n  booktitle = {arXiv},\\n  year      = {2023},\\n  url_paper = {https://arxiv.org/abs/2306.10051},\\n  abstract  = {This paper describes TOBY, a visualization tool that helps a user explore the contents of an academic survey paper. The visualization consists of four components: a hierarchical view of taxonomic data in the survey, a document similarity view in the space of taxonomic classes, a network view of citations, and a new paper recommendation tool. In this paper, we will discuss these features in the context of three separate deployments of the tool.}\\n}\\n\\n\",\"author_short\":[\"Chakraborti, T.\",\"Kang, J.\",\"Muise, C.\",\"Sreedharan, S.\",\"Walker, M.\",\"Szafir, D.\",\"Williams, T.\"],\"key\":\"chakraborti-arxiv-2023\",\"id\":\"chakraborti-arxiv-2023\",\"bibbaseid\":\"chakraborti-kang-muise-sreedharan-walker-szafir-williams-tobyatoolforexploringdatainacademicsurveypapers-2023\",\"role\":\"author\",\"urls\":{\" paper\":\"https://arxiv.org/abs/2306.10051\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"author\":[{\"firstnames\":[\"Masataro\"],\"propositions\":[],\"lastnames\":[\"Asai\"],\"suffixes\":[]},{\"firstnames\":[\"Hiroshi\"],\"propositions\":[],\"lastnames\":[\"Kajino\"],\"suffixes\":[]},{\"firstnames\":[\"Alex\"],\"propositions\":[],\"lastnames\":[\"Fukunaga\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]}],\"title\":\"Classical Planning in Deep Latent Space\",\"journal\":\"Journal of Artificial Intelligence Research\",\"volume\":\"74\",\"pages\":\"1599–1686\",\"year\":\"2022\",\"doi\":\"10.1613/jair.1.13768\",\"url_paper\":\"https://www.jair.org/index.php/jair/article/view/13768\",\"abstract\":\"Current domain-independent, classical planners require symbolic models of the problem domain and instance as input, resulting in a knowledge acquisition bottleneck. Meanwhile, although deep learning has achieved significant success in many fields, the knowledge is encoded in a subsymbolic representation which is incompatible with symbolic systems such as planners. We propose Latplan, an unsupervised architecture combining deep learning and classical planning. Given only an unlabeled set of image pairs showing a subset of transitions allowed in the environment (training inputs), Latplan learns a complete propositional PDDL action model of the environment. Later, when a pair of images representing the initial and the goal states (planning inputs) is given, Latplan finds a plan to the goal state in a symbolic latent space and returns a visualized plan execution. We evaluate Latplan using image-based versions of 6 planning domains: 8-puzzle, 15-Puzzle, Blocksworld, Sokoban and Two variations of LightsOut.\",\"bibtex\":\"@article{DBLP:journals/jair/AsaiKFM22,\\n  author    = {Masataro Asai and\\n               Hiroshi Kajino and\\n               Alex Fukunaga and\\n               Christian Muise},\\n  title     = {Classical Planning in Deep Latent Space},\\n  journal   = {Journal of Artificial Intelligence Research},\\n  volume    = {74},\\n  pages     = {1599--1686},\\n  year      = {2022},\\n  doi       = {10.1613/jair.1.13768},\\n  url_paper = {https://www.jair.org/index.php/jair/article/view/13768},\\n  abstract  = {Current domain-independent, classical planners require symbolic models of the problem domain and instance as input, resulting in a knowledge acquisition bottleneck. Meanwhile, although deep learning has achieved significant success in many fields, the knowledge is encoded in a subsymbolic representation which is incompatible with symbolic systems such as planners. We propose Latplan, an unsupervised architecture combining deep learning and classical planning. Given only an unlabeled set of image pairs showing a subset of transitions allowed in the environment (training inputs), Latplan learns a complete propositional PDDL action model of the environment. Later, when a pair of images representing the initial and the goal states (planning inputs) is given, Latplan finds a plan to the goal state in a symbolic latent space and returns a visualized plan execution. We evaluate Latplan using image-based versions of 6 planning domains: 8-puzzle, 15-Puzzle, Blocksworld, Sokoban and Two variations of LightsOut.}\\n}\\n\\n\",\"author_short\":[\"Asai, M.\",\"Kajino, H.\",\"Fukunaga, A.\",\"Muise, C.\"],\"key\":\"DBLP:journals/jair/AsaiKFM22\",\"id\":\"DBLP:journals/jair/AsaiKFM22\",\"bibbaseid\":\"asai-kajino-fukunaga-muise-classicalplanningindeeplatentspace-2022\",\"role\":\"author\",\"urls\":{\" paper\":\"https://www.jair.org/index.php/jair/article/view/13768\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":7,\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"title\":\"A Planning based Neural-Symbolic Approach for Embodied Instruction Following\",\"author\":[{\"firstnames\":[\"Xiaotian\"],\"propositions\":[],\"lastnames\":[\"Liu\"],\"suffixes\":[]},{\"firstnames\":[\"Hector\"],\"propositions\":[],\"lastnames\":[\"Palacios\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]}],\"booktitle\":\"CVPR Embodied AI Workshop\",\"year\":\"2022\",\"url_paper\":\"https://embodied-ai.org/papers/2022/15.pdf\",\"abstract\":\"The ALFRED environment features an embodied agent following instructions and accomplishing tasks in simulated home environments. However, end-to-end deep learning methods struggle at these tasks due to long-horizon and sparse rewards. In this work, we propose a principled neural-symbolic approach combining symbolic planning and deep-learning methods for visual perception and NL processing. The symbolic model is enriched as exploration progress until a full plan can be obtained. New perceptions are added to a discrete graph representation that is used for producing new planning problems. Empirical results demonstrate that our approach can achieve high scalability with SOTA performance of 36.04% unseen success rate in the ALFRED benchmark. Our work builds a foundation for a neural-symbolic approach that can act in unstructured environments when the set of skills and possible relationships is known.\",\"bibtex\":\"@incollection{liu-eai-2022,\\n  title     = {A Planning based Neural-Symbolic Approach for Embodied Instruction Following},\\n  author    = {Xiaotian Liu and Hector Palacios and Christian Muise},\\n  booktitle = {CVPR Embodied AI Workshop},\\n  year      = {2022},\\n  url_paper = {https://embodied-ai.org/papers/2022/15.pdf},\\n  abstract  = {The ALFRED environment features an embodied agent following instructions and accomplishing tasks in simulated home environments. However, end-to-end deep learning methods struggle at these tasks due to long-horizon and sparse rewards. In this work, we propose a principled neural-symbolic approach combining symbolic planning and deep-learning methods for visual perception and NL processing. The symbolic model is enriched as exploration progress until a full plan can be obtained. New perceptions are added to a discrete graph representation that is used for producing new planning problems. Empirical results demonstrate that our approach can achieve high scalability with SOTA performance of 36.04% unseen success rate in the ALFRED benchmark. Our work builds a foundation for a neural-symbolic approach that can act in unstructured environments when the set of skills and possible relationships is known.}\\n}\\n\\n\",\"author_short\":[\"Liu, X.\",\"Palacios, H.\",\"Muise, C.\"],\"key\":\"liu-eai-2022\",\"id\":\"liu-eai-2022\",\"bibbaseid\":\"liu-palacios-muise-aplanningbasedneuralsymbolicapproachforembodiedinstructionfollowing-2022\",\"role\":\"author\",\"urls\":{\" paper\":\"https://embodied-ai.org/papers/2022/15.pdf\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":7,\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"title\":\"MACQ: A Unified Library for Action Model Acquisition\",\"author\":[{\"firstnames\":[\"Ethan\"],\"propositions\":[],\"lastnames\":[\"Callanan\"],\"suffixes\":[]},{\"firstnames\":[\"Rebecca\",\"De\"],\"propositions\":[],\"lastnames\":[\"Venezia\"],\"suffixes\":[]},{\"firstnames\":[\"Victoria\"],\"propositions\":[],\"lastnames\":[\"Armstrong\"],\"suffixes\":[]},{\"firstnames\":[\"Alison\"],\"propositions\":[],\"lastnames\":[\"Paredes\"],\"suffixes\":[]},{\"firstnames\":[\"Jungkoo\"],\"propositions\":[],\"lastnames\":[\"Kang\"],\"suffixes\":[]},{\"firstnames\":[\"Tathagata\"],\"propositions\":[],\"lastnames\":[\"Chakraborti\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]}],\"booktitle\":\"International Conference on Automated Planning and Scheduling: System Demonstrations\",\"year\":\"2022\",\"url_paper\":\"http://icaps22.icaps-conference.org/demos/ICAPS_2022_paper_378.pdf\",\"abstract\":\"For over three decades, the planning community has explored countless methods for data-driven model acquisition. These range in sophistication (e.g., simple set operations to full-blown reformulations), methodology (e.g., logic-based -vs- planning-based), and assumptions (e.g., fully -vs- partially observable). With no fewer than 43 publications in the space, it can be overwhelming to understand what approach could or should be applied in a new setting. We present a holistic characterization of the action model acquisition space and further introduce a unifying framework for automated action model acquisition. We have re-implemented some of the landmark approaches in the area, and our characterization of all the techniques offers deep insight into the research opportunities that remain; i.e., those settings where no technique is capable of solving.\",\"bibtex\":\"@incollection{callanan-icapsdemo-2022,\\n  title     = {MACQ: A Unified Library for Action Model Acquisition},\\n  author    = {Ethan Callanan and Rebecca De Venezia and Victoria Armstrong and Alison Paredes and Jungkoo Kang and Tathagata Chakraborti and Christian Muise},\\n  booktitle = {International Conference on Automated Planning and Scheduling: System Demonstrations},\\n  year      = {2022},\\n  url_paper = {http://icaps22.icaps-conference.org/demos/ICAPS_2022_paper_378.pdf},\\n  abstract  = {For over three decades, the planning community has explored countless methods for data-driven model acquisition. These range in sophistication (e.g., simple set operations to full-blown reformulations), methodology (e.g., logic-based -vs- planning-based), and assumptions (e.g., fully -vs- partially observable). With no fewer than 43 publications in the space, it can be overwhelming to understand what approach could or should be applied in a new setting. We present a holistic characterization of the action model acquisition space and further introduce a unifying framework for automated action model acquisition. We have re-implemented some of the landmark approaches in the area, and our characterization of all the techniques offers deep insight into the research opportunities that remain; i.e., those settings where no technique is capable of solving.}\\n}\\n\\n\",\"author_short\":[\"Callanan, E.\",\"Venezia, R. D.\",\"Armstrong, V.\",\"Paredes, A.\",\"Kang, J.\",\"Chakraborti, T.\",\"Muise, C.\"],\"key\":\"callanan-icapsdemo-2022\",\"id\":\"callanan-icapsdemo-2022\",\"bibbaseid\":\"callanan-venezia-armstrong-paredes-kang-chakraborti-muise-macqaunifiedlibraryforactionmodelacquisition-2022\",\"role\":\"author\",\"urls\":{\" paper\":\"http://icaps22.icaps-conference.org/demos/ICAPS_2022_paper_378.pdf\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":7,\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"title\":\"Planning Tech for Planning Pedagogy\",\"author\":[{\"firstnames\":[\"Alex\"],\"propositions\":[],\"lastnames\":[\"Coulter\"],\"suffixes\":[]},{\"firstnames\":[\"Teo\"],\"propositions\":[],\"lastnames\":[\"Ilie\"],\"suffixes\":[]},{\"firstnames\":[\"Renee\"],\"propositions\":[],\"lastnames\":[\"Tibando\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]}],\"booktitle\":\"International Conference on Automated Planning and Scheduling: System Demonstrations\",\"year\":\"2022\",\"url_paper\":\"http://icaps22.icaps-conference.org/demos/ICAPS_2022_paper_376.pdf\",\"abstract\":\"We demonstrate the power planning techniques can have for the task of analyzing planning solutions in a classroom setting. Using the common assignment strategy of asking students to develop PDDL given an English description of a domain, we consider how a variety of planning methods (existing and new) can provide analytic support for teaching staff to understand which errors were made in student models. The work has already had a direct and practical impact, being deployed in a classroom setting to assess the correctness of student-authored planning models.\",\"bibtex\":\"@incollection{coulter-icapsdemo-2022,\\n  title     = {Planning Tech for Planning Pedagogy},\\n  author    = {Alex Coulter and Teo Ilie and Renee Tibando and Christian Muise},\\n  booktitle = {International Conference on Automated Planning and Scheduling: System Demonstrations},\\n  year      = {2022},\\n  url_paper = {http://icaps22.icaps-conference.org/demos/ICAPS_2022_paper_376.pdf},\\n  abstract  = {We demonstrate the power planning techniques can have for the task of analyzing planning solutions in a classroom setting. Using the common assignment strategy of asking students to develop PDDL given an English description of a domain, we consider how a variety of planning methods (existing and new) can provide analytic support for teaching staff to understand which errors were made in student models. The work has already had a direct and practical impact, being deployed in a classroom setting to assess the correctness of student-authored planning models.}\\n}\\n\\n\",\"author_short\":[\"Coulter, A.\",\"Ilie, T.\",\"Tibando, R.\",\"Muise, C.\"],\"key\":\"coulter-icapsdemo-2022\",\"id\":\"coulter-icapsdemo-2022\",\"bibbaseid\":\"coulter-ilie-tibando-muise-planningtechforplanningpedagogy-2022\",\"role\":\"author\",\"urls\":{\" paper\":\"http://icaps22.icaps-conference.org/demos/ICAPS_2022_paper_376.pdf\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"title\":\"PLANUTILS: Bringing Planning to the Masses\",\"author\":[{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Florian\"],\"propositions\":[],\"lastnames\":[\"Pommerening\"],\"suffixes\":[]},{\"firstnames\":[\"Jendrik\"],\"propositions\":[],\"lastnames\":[\"Seipp\"],\"suffixes\":[]},{\"firstnames\":[\"Michael\"],\"propositions\":[],\"lastnames\":[\"Katz\"],\"suffixes\":[]}],\"booktitle\":\"International Conference on Automated Planning and Scheduling: System Demonstrations\",\"year\":\"2022\",\"url_paper\":\"http://icaps22.icaps-conference.org/demos/ICAPS_2022_paper_377.pdf\",\"abstract\":\"PLANUTILS is a general library for setting up Linux-based environments for developing, running, and evaluating planners. Over the last decades, the planning community has produced countless solvers for various planning formalisms, as well as many other tools to help the planning practitioner. From state-of-the-art planners, over validators, to parsing libraries, the planning ecosystem has grown quite large. In the demo, we highlight an effort that aims to unify this ecosystem and make it seamless for users to get started with what the ICAPS community has to offer.\",\"bibtex\":\"@incollection{muise-icapsdemo-2022,\\n  title     = {PLANUTILS: Bringing Planning to the Masses},\\n  author    = {Christian Muise and Florian Pommerening and Jendrik Seipp and Michael Katz},\\n  booktitle = {International Conference on Automated Planning and Scheduling: System Demonstrations},\\n  year      = {2022},\\n  url_paper = {http://icaps22.icaps-conference.org/demos/ICAPS_2022_paper_377.pdf},\\n  abstract  = {PLANUTILS is a general library for setting up Linux-based environments for developing, running, and evaluating planners. Over the last decades, the planning community has produced countless solvers for various planning formalisms, as well as many other tools to help the planning practitioner. From state-of-the-art planners, over validators, to parsing libraries, the planning ecosystem has grown quite large. In the demo, we highlight an effort that aims to unify this ecosystem and make it seamless for users to get started with what the ICAPS community has to offer.}\\n}\\n\\n\",\"author_short\":[\"Muise, C.\",\"Pommerening, F.\",\"Seipp, J.\",\"Katz, M.\"],\"key\":\"muise-icapsdemo-2022\",\"id\":\"muise-icapsdemo-2022\",\"bibbaseid\":\"muise-pommerening-seipp-katz-planutilsbringingplanningtothemasses-2022\",\"role\":\"author\",\"urls\":{\" paper\":\"http://icaps22.icaps-conference.org/demos/ICAPS_2022_paper_377.pdf\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":5,\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"title\":\"TattleTale: Storytelling with Planning and Large Language Models\",\"author\":[{\"firstnames\":[\"Nisha\"],\"propositions\":[],\"lastnames\":[\"Simon\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]}],\"booktitle\":\"ICAPS Workshop on Scheduling and Planning Applications woRKshop\",\"year\":\"2022\",\"url_paper\":\"http://icaps22.icaps-conference.org/workshops/SPARK/papers/spark2022_paper_2.pdf\",\"abstract\":\"We explore how automated planning can be applied to Natural Language text generation in order to create narratives (stories) that are coherent and believable. While Large Language Models (LLMs) such as GPT-3 can be used for narrative generation based on given input prompts, they lack coherence and can be prone to repetition and stilted language. We demonstrate the use of a planning model that provides scaffolding to an LLM so that its language generation is contextdependent in order to create more coherent and believable stories in a variety of domains. After manually extracting characters, objects, and locations from the story source, we create domain and problem encoding that captures the mechanics of the story. The output of a planner, taken one action at a time, is fed to the LLM to generate a narrative. We f ind that almost all nouns (characters, objects, and locations) and verbs (actions) of the plan are reflected in the generated story, and the resulting narrative is more coherent than stories that are generated using only plain text prompts to the LLM. Finally, gathering, curating, and modelling the source stories in PDDL is an additional contribution of our work that will be released publicly. Our work represents a key first step towards the novel application of planning technology to a neuro-symbolic approach for effective story generation\",\"bibtex\":\"@incollection{simon-spark-2022,\\n  title     = {TattleTale: Storytelling with Planning and Large Language Models},\\n  author    = {Nisha Simon and Christian Muise},\\n  booktitle = {ICAPS Workshop on Scheduling and Planning Applications woRKshop},\\n  year      = {2022},\\n  url_paper = {http://icaps22.icaps-conference.org/workshops/SPARK/papers/spark2022_paper_2.pdf},\\n  abstract  = {We explore how automated planning can be applied to Natural Language text generation in order to create narratives (stories) that are coherent and believable. While Large Language Models (LLMs) such as GPT-3 can be used for narrative generation based on given input prompts, they lack coherence and can be prone to repetition and stilted language. We demonstrate the use of a planning model that provides scaffolding to an LLM so that its language generation is contextdependent in order to create more coherent and believable stories in a variety of domains. After manually extracting characters, objects, and locations from the story source, we create domain and problem encoding that captures the mechanics of the story. The output of a planner, taken one action at a time, is fed to the LLM to generate a narrative. We f ind that almost all nouns (characters, objects, and locations) and verbs (actions) of the plan are reflected in the generated story, and the resulting narrative is more coherent than stories that are generated using only plain text prompts to the LLM. Finally, gathering, curating, and modelling the source stories in PDDL is an additional contribution of our work that will be released publicly. Our work represents a key first step towards the novel application of planning technology to a neuro-symbolic approach for effective story generation}\\n}\\n\\n\",\"author_short\":[\"Simon, N.\",\"Muise, C.\"],\"key\":\"simon-spark-2022\",\"id\":\"simon-spark-2022\",\"bibbaseid\":\"simon-muise-tattletalestorytellingwithplanningandlargelanguagemodels-2022\",\"role\":\"author\",\"urls\":{\" paper\":\"http://icaps22.icaps-conference.org/workshops/SPARK/papers/spark2022_paper_2.pdf\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":11,\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"title\":\"Theory Alignment via a Classical Encoding of Regular Bisimulation\",\"author\":[{\"firstnames\":[\"Alex\"],\"propositions\":[],\"lastnames\":[\"Coulter\"],\"suffixes\":[]},{\"firstnames\":[\"Teo\"],\"propositions\":[],\"lastnames\":[\"Ilie\"],\"suffixes\":[]},{\"firstnames\":[\"Renee\"],\"propositions\":[],\"lastnames\":[\"Tibando\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]}],\"booktitle\":\"Workshop on Knowledge Engineering for Planning and Scheduling\",\"year\":\"2022\",\"url_paper\":\"http://icaps22.icaps-conference.org/workshops/KEPS/KEPS-22_paper_7781.pdf\",\"abstract\":\"Bisimulation, at its core, is a means of studying the alignment between two dynamical systems. It has been used to great effect in the planning community for heuristic computation; simulating the full state space in the space of abstractions (merge-and-shrink heuristics). Here, we consider the direct task of theory alignment– assessing if two planning problems are “equivalent”– through the lens of regular bisimulation. We accomplish the task through a novel encoding that merges the two theories as a new planning problem, where the encoded problem is unsolvable if and only if the two theories are a regular bisimulation. We demonstrate that modern planners are capable of solving many of these encodings, and the solutions (if plans exist) provide a rich explanation as to whytwomodels differ. The work has already had a direct and practical impact, being deployed in a classroom setting to assess the correctness of student-authored planning models as compared against a reference solution. Our solution has a direct impact on being able to verify if a candidate planning model matches a known specification, and opens the door to model verification through planning techniques.\",\"bibtex\":\"@incollection{coulter-keps-2022,\\n  title     = {Theory Alignment via a Classical Encoding of Regular Bisimulation},\\n  author    = {Alex Coulter and Teo Ilie and Renee Tibando and Christian Muise},\\n  booktitle = {Workshop on Knowledge Engineering for Planning and Scheduling},\\n  year      = {2022},\\n  url_paper = {http://icaps22.icaps-conference.org/workshops/KEPS/KEPS-22_paper_7781.pdf},\\n  abstract  = {Bisimulation, at its core, is a means of studying the alignment between two dynamical systems. It has been used to great effect in the planning community for heuristic computation; simulating the full state space in the space of abstractions (merge-and-shrink heuristics). Here, we consider the direct task of theory alignment– assessing if two planning problems are “equivalent”– through the lens of regular bisimulation. We accomplish the task through a novel encoding that merges the two theories as a new planning problem, where the encoded problem is unsolvable if and only if the two theories are a regular bisimulation. We demonstrate that modern planners are capable of solving many of these encodings, and the solutions (if plans exist) provide a rich explanation as to whytwomodels differ. The work has already had a direct and practical impact, being deployed in a classroom setting to assess the correctness of student-authored planning models as compared against a reference solution. Our solution has a direct impact on being able to verify if a candidate planning model matches a known specification, and opens the door to model verification through planning techniques.}\\n}\\n\\n\",\"author_short\":[\"Coulter, A.\",\"Ilie, T.\",\"Tibando, R.\",\"Muise, C.\"],\"key\":\"coulter-keps-2022\",\"id\":\"coulter-keps-2022\",\"bibbaseid\":\"coulter-ilie-tibando-muise-theoryalignmentviaaclassicalencodingofregularbisimulation-2022\",\"role\":\"author\",\"urls\":{\" paper\":\"http://icaps22.icaps-conference.org/workshops/KEPS/KEPS-22_paper_7781.pdf\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":5,\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"title\":\"Discrete Time Series Clustering and Delineation: A Tree-Based Approach to Linear Temporal Logic Discovery\",\"author\":[{\"firstnames\":[\"Brennan\"],\"propositions\":[],\"lastnames\":[\"Cruse\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]}],\"booktitle\":\"The International Workshop of Explainable AI Planning\",\"year\":\"2022\",\"url_paper\":\"https://openreview.net/forum?id=nil-578ERCL\",\"abstract\":\"Inferring temporal logic specifications from plan traces can offer significant insight into several aspects of planning such as goal recognition, policy summarization, and system dynamic modelling. Prior work in this area has predominantly focused on the identification of specifications that satisfy all plan traces within a set, however more recently, contrastive approaches concerning the delineation of two sets have also been established. While these approaches are effective in their defined scope, they assume the existence of only one or two behavioural clusters. In this paper, we re-imagine contrastive specification learning by proposing a novel tree generation technique which allows k clusters to be discovered. By embracing a Monte Carlo node-splitting approach, our algorithm seeks balance to contrastively divide any given set of plan traces into two sets with an accompanying temporal logic specification satisfying one of the sets. Recursing this procedure, we demonstrate the effectiveness of our approach to cluster and delineate plan traces, allowing temporal logic specifications to evoke insight at each level of the resulting tree.\",\"bibtex\":\"@incollection{cruse-xaip-2022,\\n  title     = {Discrete Time Series Clustering and Delineation: A Tree-Based Approach to Linear Temporal Logic Discovery},\\n  author    = {Brennan Cruse and Christian Muise},\\n  booktitle = {The International Workshop of Explainable AI Planning},\\n  year      = {2022},\\n  url_paper = {https://openreview.net/forum?id=nil-578ERCL},\\n  abstract  = {Inferring temporal logic specifications from plan traces can offer significant insight into several aspects of planning such as goal recognition, policy summarization, and system dynamic modelling. Prior work in this area has predominantly focused on the identification of specifications that satisfy all plan traces within a set, however more recently, contrastive approaches concerning the delineation of two sets have also been established. While these approaches are effective in their defined scope, they assume the existence of only one or two behavioural clusters. In this paper, we re-imagine contrastive specification learning by proposing a novel tree generation technique which allows k clusters to be discovered. By embracing a Monte Carlo node-splitting approach, our algorithm seeks balance to contrastively divide any given set of plan traces into two sets with an accompanying temporal logic specification satisfying one of the sets. Recursing this procedure, we demonstrate the effectiveness of our approach to cluster and delineate plan traces, allowing temporal logic specifications to evoke insight at each level of the resulting tree.}\\n}\\n\\n\",\"author_short\":[\"Cruse, B.\",\"Muise, C.\"],\"key\":\"cruse-xaip-2022\",\"id\":\"cruse-xaip-2022\",\"bibbaseid\":\"cruse-muise-discretetimeseriesclusteringanddelineationatreebasedapproachtolineartemporallogicdiscovery-2022\",\"role\":\"author\",\"urls\":{\" paper\":\"https://openreview.net/forum?id=nil-578ERCL\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"title\":\"Why Did You Do That? Generalizing Causal Link Explanations to Fully Observable Non-Deterministic Planning Problems\",\"author\":[{\"firstnames\":[\"Sarath\"],\"propositions\":[],\"lastnames\":[\"Sreedharan\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Subbarao\"],\"propositions\":[],\"lastnames\":[\"Kambhampati\"],\"suffixes\":[]}],\"booktitle\":\"The International Workshop of Explainable AI Planning\",\"year\":\"2022\",\"url_paper\":\"https://openreview.net/forum?id=D44ytXrLXuS\",\"abstract\":\"The problem of designing automated agents, particularly automated planning agents that can explain their decisions has been receiving a lot of attention in recent years. The field of explainable planning or XAIP has already made a lot of progress in recent years and many of them centered around the problem of explaining decisions derived for classical planning problems. As the field progresses there is interest in tackling problems from more complex planning formalisms. However, one important aspect to keep in mind as we start focusing on such settings is that the explanatory challenges we study in the context of classical planning problems do not disappear when we move to more general settings but are just magnified. As such, when we move to these more general settings, a significant challenge before us is to see how one could generalize the well-established methods studied in the context of classical planning problems to these new settings. To provide a concrete example for this new research program we will start with causal link explanations, one of the earliest and most widely used explanations techniques used in the context of policies generated for fully observable non-deterministic planning problems. This would see us generalizing a concept that was originally developed for a specific solution concept, i.e, sequential plans, and see them applied to a very different solution concept (i.e. policies). We will develop a compilation-based method for generating generalized causal link explanations and show how as the domain is limited to deterministic cases, our method would generate causal link chains as identified by earlier works.\",\"bibtex\":\"@incollection{sreedharan-xaip-2022,\\n  title     = {Why Did You Do That? Generalizing Causal Link Explanations to Fully Observable Non-Deterministic Planning Problems},\\n  author    = {Sarath Sreedharan and Christian Muise and Subbarao Kambhampati},\\n  booktitle = {The International Workshop of Explainable AI Planning},\\n  year      = {2022},\\n  url_paper = {https://openreview.net/forum?id=D44ytXrLXuS},\\n  abstract  = {The problem of designing automated agents, particularly automated planning agents that can explain their decisions has been receiving a lot of attention in recent years. The field of explainable planning or XAIP has already made a lot of progress in recent years and many of them centered around the problem of explaining decisions derived for classical planning problems. As the field progresses there is interest in tackling problems from more complex planning formalisms. However, one important aspect to keep in mind as we start focusing on such settings is that the explanatory challenges we study in the context of classical planning problems do not disappear when we move to more general settings but are just magnified. As such, when we move to these more general settings, a significant challenge before us is to see how one could generalize the well-established methods studied in the context of classical planning problems to these new settings. To provide a concrete example for this new research program we will start with causal link explanations, one of the earliest and most widely used explanations techniques used in the context of policies generated for fully observable non-deterministic planning problems. This would see us generalizing a concept that was originally developed for a specific solution concept, i.e, sequential plans, and see them applied to a very different solution concept (i.e. policies). We will develop a compilation-based method for generating generalized causal link explanations and show how as the domain is limited to deterministic cases, our method would generate causal link chains as identified by earlier works.}\\n}\\n\\n\",\"author_short\":[\"Sreedharan, S.\",\"Muise, C.\",\"Kambhampati, S.\"],\"key\":\"sreedharan-xaip-2022\",\"id\":\"sreedharan-xaip-2022\",\"bibbaseid\":\"sreedharan-muise-kambhampati-whydidyoudothatgeneralizingcausallinkexplanationstofullyobservablenondeterministicplanningproblems-2022\",\"role\":\"author\",\"urls\":{\" paper\":\"https://openreview.net/forum?id=D44ytXrLXuS\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"title\":\"Planning to Avoid Side Effects\",\"author\":[{\"firstnames\":[\"Toryn\",\"Q.\"],\"propositions\":[],\"lastnames\":[\"Klassen\"],\"suffixes\":[]},{\"firstnames\":[\"Sheila\",\"A.\"],\"propositions\":[],\"lastnames\":[\"McIlraith\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Jarvis\"],\"propositions\":[],\"lastnames\":[\"Xu\"],\"suffixes\":[]}],\"booktitle\":\"AAAI Conference on Artificial Intelligence\",\"pages\":\"9830–9839\",\"year\":\"2022\",\"url_paper\":\"https://ojs.aaai.org/index.php/AAAI/article/view/21219\",\"abstract\":\"In sequential decision making, objective specifications are often underspecified or incomplete, neglecting to take into account potential (negative) side effects. Executing plans without consideration of their side effects can lead to catastrophic outcomes – a concern recently raised in relation to the safety of AI. In this paper we investigate how to avoid side effects in a symbolic planning setting. We study the notion of minimizing side effects in the context of a planning environment where multiple independent agents co-exist. We define (classes of) negative side effects in terms of their effect on the agency of those other agents. Finally, we show how plans which minimize side effects of different types can be computed via compilations to cost-optimizing symbolic planning, and investigate experimentally.\",\"bibtex\":\"@incollection{klassen-aaai-2022,\\n  title     = {Planning to Avoid Side Effects},\\n  author    = {Toryn Q. Klassen and Sheila A. McIlraith and Christian Muise and Jarvis Xu},\\n  booktitle = {AAAI Conference on Artificial Intelligence},\\n  pages     = {9830--9839},\\n  year      = {2022},\\n  url_paper = {https://ojs.aaai.org/index.php/AAAI/article/view/21219},\\n  abstract  = {In sequential decision making, objective specifications are often underspecified or incomplete, neglecting to take into account potential (negative) side effects. Executing plans without consideration of their side effects can lead to catastrophic outcomes -- a concern recently raised in relation to the safety of AI. In this paper we investigate how to avoid side effects in a symbolic planning setting. We study the notion of minimizing side effects in the context of a planning environment where multiple independent agents co-exist. We define (classes of) negative side effects in terms of their effect on the agency of those other agents. Finally, we show how plans which minimize side effects of different types can be computed via compilations to cost-optimizing symbolic planning, and investigate experimentally.}\\n}\\n\\n\",\"author_short\":[\"Klassen, T. Q.\",\"McIlraith, S. A.\",\"Muise, C.\",\"Xu, J.\"],\"key\":\"klassen-aaai-2022\",\"id\":\"klassen-aaai-2022\",\"bibbaseid\":\"klassen-mcilraith-muise-xu-planningtoavoidsideeffects-2022\",\"role\":\"author\",\"urls\":{\" paper\":\"https://ojs.aaai.org/index.php/AAAI/article/view/21219\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":11,\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"title\":\"Permutation-Invariant Representation of Neural Networks with Neuron Embeddings\",\"author\":[{\"firstnames\":[\"Ryan\"],\"propositions\":[],\"lastnames\":[\"Zhou\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Ting\"],\"propositions\":[],\"lastnames\":[\"Hu\"],\"suffixes\":[]}],\"booktitle\":\"European Conference on Genetic Programming\",\"pages\":\"294–-308\",\"year\":\"2022\",\"url_paper\":\"https://link.springer.com/chapter/10.1007/978-3-031-02056-8_19\",\"abstract\":\"Neural networks are traditionally represented in terms of their weights. A key property of this representation is that there are multiple representations of a network which can be obtained by permuting the order of the neurons. These representations are generally not compatible between networks, making recombination a challenge for two arbitrary neural networks - an issue known as the “permutation problem” in neuroevolution. This paper proposes an indirect encoding in which a neural network is represented in terms of interactions between neurons rather than explicit weights, and which works for both fully connected and convolutional networks. In addition to reducing the number of free parameters, this encoding is agnostic to the ordering of neurons, bypassing a key problem for direct weight-based representation. This allows us to transplant individual neurons and layers into another network without accounting for the specific ordering of neurons. We show through experiments on the MNIST and CIFAR-10 datasets that this method is capable of representing networks which achieve comparable performance to direct weight representation, and that combining networks this way preserves a larger degree of performance than through direct weight transfer.\",\"bibtex\":\"@incollection{zhou-eurogp-2022,\\n  title     = {Permutation-Invariant Representation of Neural Networks with Neuron Embeddings},\\n  author    = {Ryan Zhou and Christian Muise and Ting Hu},\\n  booktitle = {European Conference on Genetic Programming},\\n  pages     = {294–-308},\\n  year      = {2022},\\n  url_paper = {https://link.springer.com/chapter/10.1007/978-3-031-02056-8_19},\\n  abstract  = {Neural networks are traditionally represented in terms of their weights. A key property of this representation is that there are multiple representations of a network which can be obtained by permuting the order of the neurons. These representations are generally not compatible between networks, making recombination a challenge for two arbitrary neural networks - an issue known as the “permutation problem” in neuroevolution. This paper proposes an indirect encoding in which a neural network is represented in terms of interactions between neurons rather than explicit weights, and which works for both fully connected and convolutional networks. In addition to reducing the number of free parameters, this encoding is agnostic to the ordering of neurons, bypassing a key problem for direct weight-based representation. This allows us to transplant individual neurons and layers into another network without accounting for the specific ordering of neurons. We show through experiments on the MNIST and CIFAR-10 datasets that this method is capable of representing networks which achieve comparable performance to direct weight representation, and that combining networks this way preserves a larger degree of performance than through direct weight transfer.}\\n}\\n\\n\",\"author_short\":[\"Zhou, R.\",\"Muise, C.\",\"Hu, T.\"],\"key\":\"zhou-eurogp-2022\",\"id\":\"zhou-eurogp-2022\",\"bibbaseid\":\"zhou-muise-hu-permutationinvariantrepresentationofneuralnetworkswithneuronembeddings-2022\",\"role\":\"author\",\"urls\":{\" paper\":\"https://link.springer.com/chapter/10.1007/978-3-031-02056-8_19\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"title\":\"Efficient Multi-agent Epistemic Planning: Teaching Planners About Nested Belief\",\"author\":[{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Vaishak\"],\"propositions\":[],\"lastnames\":[\"Belle\"],\"suffixes\":[]},{\"firstnames\":[\"Paolo\"],\"propositions\":[],\"lastnames\":[\"Felli\"],\"suffixes\":[]},{\"firstnames\":[\"Sheila\"],\"propositions\":[],\"lastnames\":[\"McIlraith\"],\"suffixes\":[]},{\"firstnames\":[\"Tim\"],\"propositions\":[],\"lastnames\":[\"Miller\"],\"suffixes\":[]},{\"firstnames\":[\"Adrian\"],\"propositions\":[],\"lastnames\":[\"Pearce\"],\"suffixes\":[]},{\"firstnames\":[\"Liz\"],\"propositions\":[],\"lastnames\":[\"Sonenberg\"],\"suffixes\":[]}],\"booktitle\":\"International Conference on Automated Planning and Scheduling: Journal Track\",\"year\":\"2022\",\"url_paper\":\"https://arxiv.org/pdf/2110.02480.pdf\",\"abstract\":\"Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology for solving efficiently. Our approach represents an important step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents.\",\"bibtex\":\"@incollection{muise-icapsjournal-2022,\\n  title     = {Efficient Multi-agent Epistemic Planning: Teaching Planners About Nested Belief},\\n  author    = {Christian Muise and Vaishak Belle and Paolo Felli and Sheila McIlraith and Tim Miller and Adrian Pearce and Liz Sonenberg},\\n  booktitle = {International Conference on Automated Planning and Scheduling: Journal Track},\\n  year      = {2022},\\n  url_paper = {https://arxiv.org/pdf/2110.02480.pdf},\\n  abstract  = {Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology for solving efficiently. Our approach represents an important step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents.}\\n}\\n\\n\",\"author_short\":[\"Muise, C.\",\"Belle, V.\",\"Felli, P.\",\"McIlraith, S.\",\"Miller, T.\",\"Pearce, A.\",\"Sonenberg, L.\"],\"key\":\"muise-icapsjournal-2022\",\"id\":\"muise-icapsjournal-2022\",\"bibbaseid\":\"muise-belle-felli-mcilraith-miller-pearce-sonenberg-efficientmultiagentepistemicplanningteachingplannersaboutnestedbelief-2022\",\"role\":\"author\",\"urls\":{\" paper\":\"https://arxiv.org/pdf/2110.02480.pdf\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":20,\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"title\":\"An AI Safety Threat from Learned Planning Models\",\"author\":[{\"firstnames\":[\"Toryn\",\"Q.\"],\"propositions\":[],\"lastnames\":[\"Klassen\"],\"suffixes\":[]},{\"firstnames\":[\"Sheila\"],\"propositions\":[],\"lastnames\":[\"McIlraith\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]}],\"booktitle\":\"ICAPS Workshop on Reliable Data-Driven Planning and Scheduling\",\"year\":\"2022\",\"url_paper\":\"http://icaps22.icaps-conference.org/workshops/RDDPS/papers/klassen_etal_rddps22.pdf\",\"abstract\":\"Historically, planning problems have often been constructed by hand, with the domain model and the goal developed together, leading to the model and goal being in harmony in the sense that the goal describes exactly which parts of the modelled state were desired to be changed (and not changed) as a consequence of the execution of the plan. With models learned from data, human goal specifiers may not know all the aspects of the model, nor have spent much time thinking about the real world situation that is being modelled. Also, naive users may expect the goals they specify to be interpreted in a commonsensical way by the automated planning system. These things may lead human goal specifiers to more often create incomplete goal specifications, failing to take into account all the different ways the environment can be changed– the potential side effects of plans. This could threaten safety. However, learned models may in some cases also have the feature of having detailed state representations, affording the opportunity for symbolic planning algorithms to recognize side effects that their human users did not think of, and to help avoid them. We propose in this position paper that researchers in symbolic planning should take up the challenge of developing planning algorithms that can safely deal with underspecified objectives– i.e., with problem goals that fail to specify everything that people want.\",\"bibtex\":\"@incollection{klassen-rddps-2022,\\n  title     = {An AI Safety Threat from Learned Planning Models},\\n  author    = {Toryn Q. Klassen and Sheila McIlraith and Christian Muise},\\n  booktitle = {ICAPS Workshop on Reliable Data-Driven Planning and Scheduling},\\n  year      = {2022},\\n  url_paper = {http://icaps22.icaps-conference.org/workshops/RDDPS/papers/klassen_etal_rddps22.pdf},\\n  abstract  = {Historically, planning problems have often been constructed by hand, with the domain model and the goal developed together, leading to the model and goal being in harmony in the sense that the goal describes exactly which parts of the modelled state were desired to be changed (and not changed) as a consequence of the execution of the plan. With models learned from data, human goal specifiers may not know all the aspects of the model, nor have spent much time thinking about the real world situation that is being modelled. Also, naive users may expect the goals they specify to be interpreted in a commonsensical way by the automated planning system. These things may lead human goal specifiers to more often create incomplete goal specifications, failing to take into account all the different ways the environment can be changed– the potential side effects of plans. This could threaten safety. However, learned models may in some cases also have the feature of having detailed state representations, affording the opportunity for symbolic planning algorithms to recognize side effects that their human users did not think of, and to help avoid them. We propose in this position paper that researchers in symbolic planning should take up the challenge of developing planning algorithms that can safely deal with underspecified objectives– i.e., with problem goals that fail to specify everything that people want.}\\n}\\n\\n\",\"author_short\":[\"Klassen, T. Q.\",\"McIlraith, S.\",\"Muise, C.\"],\"key\":\"klassen-rddps-2022\",\"id\":\"klassen-rddps-2022\",\"bibbaseid\":\"klassen-mcilraith-muise-anaisafetythreatfromlearnedplanningmodels-2022\",\"role\":\"author\",\"urls\":{\" paper\":\"http://icaps22.icaps-conference.org/workshops/RDDPS/papers/klassen_etal_rddps22.pdf\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":38,\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"title\":\"Symbolic Reasoning in Latent Space: Classical Planning as an Example\",\"author\":[{\"firstnames\":[\"Masataro\"],\"propositions\":[],\"lastnames\":[\"Asai\"],\"suffixes\":[]},{\"firstnames\":[\"Hiroshi\"],\"propositions\":[],\"lastnames\":[\"Kajino\"],\"suffixes\":[]},{\"firstnames\":[\"Alex\"],\"propositions\":[],\"lastnames\":[\"Fukunaga\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]}],\"booktitle\":\"Neuro-Symbolic Artificial Intelligence: The State of the Art\",\"pages\":\"52–77\",\"year\":\"2022\",\"publisher\":\"IOPress\",\"url\":\"https://www.iospress.com/catalog/books/neuro-symbolic-artificial-intelligence-the-state-of-the-art\",\"abstract\":\"Symbolic systems require hand-coded symbolic representation as input, resulting in a knowledge acquisition bottleneck. Meanwhile, although deep learning has achieved significant success in many fields, the knowledge is encoded in a subsymbolic representation which is incompatible with symbolic systems. To address the gap between the two fields, one has to solve Symbol Grounding problem: The question of how a machine can generate symbols automatically. We discuss our recent work called Latplan, an unsupervised architecture combining deep learning and classical planning. Given only an unlabeled set of image pairs showing a subset of transitions allowed in the environment (training inputs), Latplan learns a complete propositional PDDL action model of the environment. Later, when a pair of images representing the initial and the goal states (planning inputs) is given, Latplan finds a plan to the goal state in a symbolic latent space and returns a visualized plan execution. We discuss several key ideas that made Latplan possible which would hopefully extend to many other symbolic paradigms outside classical planning.\",\"bibtex\":\"@incollection{nesy-book-2022,\\n  title={Symbolic Reasoning in Latent Space: Classical Planning as an Example},\\n  author={Masataro Asai and Hiroshi Kajino and Alex Fukunaga and Christian Muise},\\n  booktitle={Neuro-Symbolic Artificial Intelligence: The State of the Art},\\n  pages={52--77},\\n  year={2022},\\n  publisher={IOPress},\\n  url={https://www.iospress.com/catalog/books/neuro-symbolic-artificial-intelligence-the-state-of-the-art},\\n  abstract={Symbolic systems require hand-coded symbolic representation as input, resulting in a knowledge acquisition bottleneck. Meanwhile, although deep learning has achieved significant success in many fields, the knowledge is encoded in a subsymbolic representation which is incompatible with symbolic systems. To address the gap between the two fields, one has to solve Symbol Grounding problem: The question of how a machine can generate symbols automatically. We discuss our recent work called Latplan, an unsupervised architecture combining deep learning and classical planning. Given only an unlabeled set of image pairs showing a subset of transitions allowed in the environment (training inputs), Latplan learns a complete propositional PDDL action model of the environment. Later, when a pair of images representing the initial and the goal states (planning inputs) is given, Latplan finds a plan to the goal state in a symbolic latent space and returns a visualized plan execution. We discuss several key ideas that made Latplan possible which would hopefully extend to many other symbolic paradigms outside classical planning.}\\n}\\n\\n\\n\\n\",\"author_short\":[\"Asai, M.\",\"Kajino, H.\",\"Fukunaga, A.\",\"Muise, C.\"],\"key\":\"nesy-book-2022\",\"id\":\"nesy-book-2022\",\"bibbaseid\":\"asai-kajino-fukunaga-muise-symbolicreasoninginlatentspaceclassicalplanningasanexample-2022\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.iospress.com/catalog/books/neuro-symbolic-artificial-intelligence-the-state-of-the-art\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":3,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"firstnames\":[\"Xiaotian\"],\"propositions\":[],\"lastnames\":[\"Liu\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]}],\"title\":\"A Neural-Symbolic Approach for Object Navigation\",\"booktitle\":\"2nd Embodied AI Workshop (CVPR 2021)\",\"year\":\"2021\",\"url_paper\":\"https://embodied-ai.org/papers/A-Neural-Symbolic-Approach-for-Object-Navigation.pdf\",\"url_poster\":\"https://embodied-ai.org/posters/Neural-Symbolic-Approach-for-Object-Navigation.pdf\",\"abstract\":\"Object navigation refers to the task of discovering and locating objects in an unknown environment. End-to-end deep learning methods struggle at this task due to sparse rewards. In this work, we propose a simple neural-symbolic approach for object navigation in the AI2-THOR environment. Our method takes raw RGB images as input and uses a spatial memory graph as memory to store object and location information. The architecture consists of both a convolutional neural network for object detection and a spatial graph to represent the environment. By having a discrete graph representation of the environment, the agent can directly use search or planning algorithms as high-level reasoning engines. Model performance is evaluated on both task completion rate and steps required to reach target objects. Empirical results demonstrate that our approach can achieve performance close to the optimal. Our work builds a foundation for a neural-symbolic approach that can reason via unstructured visual cues.\",\"bibtex\":\"@inproceedings{liu-eai-2021,\\n  author    = {Xiaotian Liu and Christian Muise},\\n  title     = {A Neural-Symbolic Approach for Object Navigation},\\n  booktitle = {2nd Embodied AI Workshop (CVPR 2021)},\\n  year      = {2021},\\n  url_paper = {https://embodied-ai.org/papers/A-Neural-Symbolic-Approach-for-Object-Navigation.pdf},\\n  url_poster = {https://embodied-ai.org/posters/Neural-Symbolic-Approach-for-Object-Navigation.pdf},\\n  abstract  = {Object navigation refers to the task of discovering and locating objects in an unknown environment. End-to-end deep learning methods struggle at this task due to sparse rewards. In this work, we propose a simple neural-symbolic approach for object navigation in the AI2-THOR environment. Our method takes raw RGB images as input and uses a spatial memory graph as memory to store object and location information. The architecture consists of both a convolutional neural network for object detection and a spatial graph to represent the environment. By having a discrete graph representation of the environment, the agent can directly use search or planning algorithms as high-level reasoning engines. Model performance is evaluated on both task completion rate and steps required to reach target objects. Empirical results demonstrate that our approach can achieve performance close to the optimal. Our work builds a foundation for a neural-symbolic approach that can reason via unstructured visual cues.}\\n}\\n\\n\",\"author_short\":[\"Liu, X.\",\"Muise, C.\"],\"key\":\"liu-eai-2021\",\"id\":\"liu-eai-2021\",\"bibbaseid\":\"liu-muise-aneuralsymbolicapproachforobjectnavigation-2021\",\"role\":\"author\",\"urls\":{\" paper\":\"https://embodied-ai.org/papers/A-Neural-Symbolic-Approach-for-Object-Navigation.pdf\",\" poster\":\"https://embodied-ai.org/posters/Neural-Symbolic-Approach-for-Object-Navigation.pdf\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"firstnames\":[\"Xiaotian\"],\"propositions\":[],\"lastnames\":[\"Liu\"],\"suffixes\":[]},{\"firstnames\":[\"Victoria\"],\"propositions\":[],\"lastnames\":[\"Armstrong\"],\"suffixes\":[]},{\"firstnames\":[\"Sara\"],\"propositions\":[],\"lastnames\":[\"Nabil\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]}],\"title\":\"Exploring Multi-View Perspectives on Deep Reinforcement Learning Agents for Embodied Object Navigation in Virtual Home Environments\",\"booktitle\":\"CASCON EVOKE\",\"year\":\"2021\",\"url\":\"https://mulab.ai/papers/2021-cascon-liu-armstrong.pdf\",\"abstract\":\"Recent years have brought the exploration of embodied reinforcement learning agents in a variety of domains. One of the advantages of artificial agents is that they can obtain visual inputs simultaneously using multiple input devices. This work explores multi-view reinforcement learning for object navigation tasks in 3D rendered virtual home environments using AI2-THOR. We trained CNN based Deep Q-learning embodied agents with egocentric, allocentric, and combined egocentric-allocentric perspectives to locate an object in an unknown environment. We compared the results of the three RL agents, and evaluated them by both reward improvement rate, and reward obtained. We demonstrate that the egocentric perspective allows for faster reward accumulation in the earlier episodes, whereas the allocentric agents obtained better long-term rewards. Interesting results arise from the combined allocentric and egocentric perspective, where we found that the agent had the best overall results by harnessing the benefits of each perspective. The results show that while single perspective embodied agents each have their own advantages, combining both inputs yield the best overall reward. Our findings provide a foundation and benchmark for building embodied RL agents with multi-view perspectives.\",\"bibtex\":\"@inproceedings{liu-cascon-2021,\\n  author    = {Xiaotian Liu and Victoria Armstrong and Sara Nabil and Christian Muise},\\n  title     = {Exploring Multi-View Perspectives on Deep Reinforcement Learning Agents for Embodied Object Navigation in Virtual Home Environments},\\n  booktitle = {CASCON EVOKE},\\n  year      = {2021},\\n  url       = {https://mulab.ai/papers/2021-cascon-liu-armstrong.pdf},\\n  abstract  = {Recent years have brought the exploration of embodied reinforcement learning agents in a variety of domains. One of the advantages of artificial agents is that they can obtain visual inputs simultaneously using multiple input devices. This work explores multi-view reinforcement learning for object navigation tasks in 3D rendered virtual home environments using AI2-THOR. We trained CNN based Deep Q-learning embodied agents with egocentric, allocentric, and combined egocentric-allocentric perspectives to locate an object in an unknown environment. We compared the results of the three RL agents, and evaluated them by both reward improvement rate, and reward obtained. We demonstrate that the egocentric perspective allows for faster reward accumulation in the earlier episodes, whereas the allocentric agents obtained better long-term rewards. Interesting results arise from the combined allocentric and egocentric perspective, where we found that the agent had the best overall results by harnessing the benefits of each perspective. The results show that while single perspective embodied agents each have their own advantages, combining both inputs yield the best overall reward. Our findings provide a foundation and benchmark for building embodied RL agents with multi-view perspectives.}\\n}\\n\\n\",\"author_short\":[\"Liu, X.\",\"Armstrong, V.\",\"Nabil, S.\",\"Muise, C.\"],\"key\":\"liu-cascon-2021\",\"id\":\"liu-cascon-2021\",\"bibbaseid\":\"liu-armstrong-nabil-muise-exploringmultiviewperspectivesondeepreinforcementlearningagentsforembodiedobjectnavigationinvirtualhomeenvironments-2021\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://mulab.ai/papers/2021-cascon-liu-armstrong.pdf\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"firstnames\":[\"Nisha\"],\"propositions\":[],\"lastnames\":[\"Simon\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]}],\"title\":\"A Natural Language Model for Generating PDDL\",\"booktitle\":\"The ICAPS Workshop on Knowledge Engineering for Planning and Scheduling (KEPS)\",\"year\":\"2021\",\"url\":\"https://icaps21.icaps-conference.org/workshops/KEPS/Papers/KEPS_2021_paper_7.pdf\",\"abstract\":\"Language generation in various domains has drawn a large amount of interest in recent years. This paper studies language generation in the context of generating planning specifications in the syntax typically used for this task: the Planning Domain Definition Language (PDDL). The goal of this preliminary work is to predict the next completion in PDDL code, based on previous and surrounding text. Generating valid PDDL code is a key component in creating robust planners. Thus, the ability to generate PDDL code will be extremely useful to PDDL practitioners for the purpose of solving planning problems. It further opens the door to providing a source of inspiration for the modeller. The main contribution of our approach is a language model built using Recurrent Neural Networks (RNNs) that is trained on existing PDDL domains, which can be used to generate PDDL-like code. We train our model on a corpus of publicly available PDDL files from api.planning.domains, and evaluate our approach in the setting of PDDL auto-prediction for some of the more common domains. We found that code-like generation is possible, although fluency can be improved.\",\"bibtex\":\"@inproceedings{simon-keps-2021,\\n  author    = {Nisha Simon and Christian Muise},\\n  title     = {A Natural Language Model for Generating PDDL},\\n  booktitle = {The ICAPS Workshop on Knowledge Engineering for Planning and Scheduling (KEPS)},\\n  year      = {2021},\\n  url       = {https://icaps21.icaps-conference.org/workshops/KEPS/Papers/KEPS_2021_paper_7.pdf},\\n  abstract  = {Language generation in various domains has drawn a large amount of interest in recent years. This paper studies language generation in the context of generating planning specifications in the syntax typically used for this task: the Planning Domain Definition Language (PDDL). The goal of this preliminary work is to predict the next completion in PDDL code, based on previous and surrounding text. Generating valid PDDL code is a key component in creating robust planners. Thus, the ability to generate PDDL code will be extremely useful to PDDL practitioners for the purpose of solving planning problems. It further opens the door to providing a source of inspiration for the modeller. The main contribution of our approach is a language model built using Recurrent Neural Networks (RNNs) that is trained on existing PDDL domains, which can be used to generate PDDL-like code. We train our model on a corpus of publicly available PDDL files from api.planning.domains, and evaluate our approach in the setting of PDDL auto-prediction for some of the more common domains. We found that code-like generation is possible, although fluency can be improved.}\\n}\\n\\n\",\"author_short\":[\"Simon, N.\",\"Muise, C.\"],\"key\":\"simon-keps-2021\",\"id\":\"simon-keps-2021\",\"bibbaseid\":\"simon-muise-anaturallanguagemodelforgeneratingpddl-2021\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://icaps21.icaps-conference.org/workshops/KEPS/Papers/KEPS_2021_paper_7.pdf\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"firstnames\":[\"Caitlin\"],\"propositions\":[],\"lastnames\":[\"Aspinall\"],\"suffixes\":[]},{\"firstnames\":[\"Cam\"],\"propositions\":[],\"lastnames\":[\"Cunningham\"],\"suffixes\":[]},{\"firstnames\":[\"Ellie\"],\"propositions\":[],\"lastnames\":[\"Sekine\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]}],\"title\":\"A Planning.Domains Plugin for Heuristic Visualization\",\"booktitle\":\"System Demonstrations at the Thirty-First International Conference on Automated Planning and Scheduling (ICAPS)\",\"year\":\"2021\",\"url\":\"https://icaps21.icaps-conference.org/demos/demos/377.pdf\",\"abstract\":\"Heuristics are at the heart of every planner. From the simple to the complex, they drive both satisficing and optimal planning approaches. Despite their pervasiveness, there has been relatively little effort towards systematically visualizing them. We introduce a plugin for the online editor at Planning.Domains that is capable of visualizing the heuristic computation of a manually explored state space. Our initial implementation demonstrates the hadd heuristic, but the framework serves as an extensible base for other heuristics in the field of automated planning.\",\"bibtex\":\"@inproceedings{heurviz-icaps21demo,\\n  author    = {Caitlin Aspinall and Cam Cunningham and Ellie Sekine and Christian Muise},\\n  title     = {A Planning.Domains Plugin for Heuristic Visualization},\\n  booktitle = {System Demonstrations at the Thirty-First International Conference on Automated Planning and Scheduling ({ICAPS})},\\n  year      = {2021},\\n  url       = {https://icaps21.icaps-conference.org/demos/demos/377.pdf},\\n  abstract  = {Heuristics are at the heart of every planner. From the simple to the complex, they drive both satisficing and optimal planning approaches. Despite their pervasiveness, there has been relatively little effort towards systematically visualizing them. We introduce a plugin for the online editor at Planning.Domains that is capable of visualizing the heuristic computation of a manually explored state space. Our initial implementation demonstrates the hadd heuristic, but the framework serves as an extensible base for other heuristics in the field of automated planning.}\\n}\\n\\n\",\"author_short\":[\"Aspinall, C.\",\"Cunningham, C.\",\"Sekine, E.\",\"Muise, C.\"],\"key\":\"heurviz-icaps21demo\",\"id\":\"heurviz-icaps21demo\",\"bibbaseid\":\"aspinall-cunningham-sekine-muise-aplanningdomainspluginforheuristicvisualization-2021\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://icaps21.icaps-conference.org/demos/demos/377.pdf\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"firstnames\":[\"Xiaotian\"],\"propositions\":[],\"lastnames\":[\"Liu\"],\"suffixes\":[]},{\"firstnames\":[\"Alison\"],\"propositions\":[],\"lastnames\":[\"Paredes\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]}],\"title\":\"Do You See What I See? An Egocentric View of our Pansophical Planning Problems\",\"booktitle\":\"The ICAPS Workshop on Integrated Planning, Acting, and Execution (IntEx)\",\"url\":\"https://icaps21.icaps-conference.org/workshops/IntEx/IntEx2021proceedings_final.pdf#page=36\",\"abstract\":\"Classical planning takes a pansophical view of the world: everything is fully known, observed, and static. While there are extensions to partial observability, this leapfrogs an important intermediate step of embodied agent design: egocentricity. In this work, we propose a semi-automated mechanism that allows planning domain designers to convert classical planning problems into an egocentric alternative. The generated planning problems are classical as well, and we introduce an open-loop replanning mechanism that progressively explores the egocentric space until the original goal is solved (or deemed unsolvable). Our work serves as a crucial first step towards embodied agents that can be equipped with an appropriately specified egocentric version of known environment dynamics.\",\"year\":\"2021\",\"bibtex\":\"@inproceedings{liu2021intex,\\n  author    = {Xiaotian Liu and Alison Paredes and Christian Muise},\\n  title     = {Do You See What I See? An Egocentric View of our Pansophical Planning Problems},\\n  booktitle = {The ICAPS Workshop on Integrated Planning, Acting, and Execution (IntEx)},\\n  url       = {https://icaps21.icaps-conference.org/workshops/IntEx/IntEx2021proceedings_final.pdf#page=36},\\n  abstract  = {Classical planning takes a pansophical view of the world: everything is fully known, observed, and static. While there are extensions to partial observability, this leapfrogs an important intermediate step of embodied agent design: egocentricity. In this work, we propose a semi-automated mechanism that allows planning domain designers to convert classical planning problems into an egocentric alternative. The generated planning problems are classical as well, and we introduce an open-loop replanning mechanism that progressively explores the egocentric space until the original goal is solved (or deemed unsolvable). Our work serves as a crucial first step towards embodied agents that can be equipped with an appropriately specified egocentric version of known environment dynamics.},\\n  year      = {2021},\\n}\\n\\n\\n\",\"author_short\":[\"Liu, X.\",\"Paredes, A.\",\"Muise, C.\"],\"key\":\"liu2021intex\",\"id\":\"liu2021intex\",\"bibbaseid\":\"liu-paredes-muise-doyouseewhatiseeanegocentricviewofourpansophicalplanningproblems-2021\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://icaps21.icaps-conference.org/workshops/IntEx/IntEx2021proceedings_final.pdf#page=36\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"author\":[{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Vaishak\"],\"propositions\":[],\"lastnames\":[\"Belle\"],\"suffixes\":[]},{\"firstnames\":[\"Paolo\"],\"propositions\":[],\"lastnames\":[\"Felli\"],\"suffixes\":[]},{\"firstnames\":[\"Sheila\",\"A.\"],\"propositions\":[],\"lastnames\":[\"McIlraith\"],\"suffixes\":[]},{\"firstnames\":[\"Tim\"],\"propositions\":[],\"lastnames\":[\"Miller\"],\"suffixes\":[]},{\"firstnames\":[\"Adrian\",\"R.\"],\"propositions\":[],\"lastnames\":[\"Pearce\"],\"suffixes\":[]},{\"firstnames\":[\"Liz\"],\"propositions\":[],\"lastnames\":[\"Sonenberg\"],\"suffixes\":[]}],\"title\":\"Efficient Multi-agent Epistemic Planning: Teaching Planners About Nested Belief\",\"journal\":\"Artificial Intelligence Journal\",\"year\":\"2021\",\"url\":\"https://arxiv.org/pdf/2110.02480.pdf\",\"abstract\":\"Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology for solving efficiently. Our approach represents an important step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents.\",\"bibtex\":\"@article{muise2021aij,\\n  author    = {Christian Muise and Vaishak Belle and Paolo Felli and Sheila A. McIlraith and Tim Miller and Adrian R. Pearce and Liz Sonenberg},\\n  title     = {Efficient Multi-agent Epistemic Planning: Teaching Planners About Nested Belief},\\n  journal   = {Artificial Intelligence Journal},\\n  year      = {2021},\\n  url       = {https://arxiv.org/pdf/2110.02480.pdf},\\n  abstract  = {Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology for solving efficiently. Our approach represents an important step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents.}\\n}\\n\\n\",\"author_short\":[\"Muise, C.\",\"Belle, V.\",\"Felli, P.\",\"McIlraith, S. A.\",\"Miller, T.\",\"Pearce, A. R.\",\"Sonenberg, L.\"],\"key\":\"muise2021aij\",\"id\":\"muise2021aij\",\"bibbaseid\":\"muise-belle-felli-mcilraith-miller-pearce-sonenberg-efficientmultiagentepistemicplanningteachingplannersaboutnestedbelief-2021\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://arxiv.org/pdf/2110.02480.pdf\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":31,\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"title\":\"KEPS Book: Planning. Domains\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Muise\"],\"firstnames\":[\"Christian\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Lipovetzky\"],\"firstnames\":[\"Nir\"],\"suffixes\":[]}],\"booktitle\":\"Knowledge Engineering Tools and Techniques for AI Planning\",\"pages\":\"91–105\",\"year\":\"2021\",\"publisher\":\"Springer\",\"bibtex\":\"@incollection{muise2021keps,\\n  title={KEPS Book: Planning. Domains},\\n  author={Muise, Christian and Lipovetzky, Nir},\\n  booktitle={Knowledge Engineering Tools and Techniques for AI Planning},\\n  pages={91--105},\\n  year={2021},\\n  publisher={Springer}\\n}\\n\\n\",\"author_short\":[\"Muise, C.\",\"Lipovetzky, N.\"],\"key\":\"muise2021keps\",\"id\":\"muise2021keps\",\"bibbaseid\":\"muise-lipovetzky-kepsbookplanningdomains-2021\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"firstnames\":[\"Qianyu\"],\"propositions\":[],\"lastnames\":[\"Zhang\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]}],\"title\":\"Action Usability via Deadend Detection\",\"booktitle\":\"The ICAPS Workshop on Knowledge Engineering for Planning and Scheduling (KEPS)\",\"year\":\"2020\",\"abstract\":\"The vast majority of planning problem models are incorrect, incomplete, or simply inconsistent. This is particularly an issue during the development process of a planning model. However, there are few existing debugging tools for modeling to aid with this issue. In this paper, we introduce a method for detecting actions that are deemed unusable, which may naturally be a result of modelling errors. We use action usability (or reachability) detection for on-the-fly diagnostics of planning models. In our technique, each action is checked for usability via problem reformulation and unsolvability detection. Through the analysis of usability, this technique could improve the modeling process directly, and we have demonstrated this capability through the tight integration with the online PDDL editor at Planning.Domains.\",\"bibtex\":\"@inproceedings{zhang-keps-2020,\\n  author    = {Qianyu Zhang and Christian Muise},\\n  title     = {Action Usability via Deadend Detection},\\n  booktitle = {The ICAPS Workshop on Knowledge Engineering for Planning and Scheduling (KEPS)},\\n  year      = {2020},\\n  abstract  = {The vast majority of planning problem models are incorrect, incomplete, or simply inconsistent. This is particularly an issue during the development process of a planning model. However, there are few existing debugging tools for modeling to aid with this issue. In this paper, we introduce a method for detecting actions that are deemed unusable, which may naturally be a result of modelling errors. We use action usability (or reachability) detection for on-the-fly diagnostics of planning models. In our technique, each action is checked for usability via problem reformulation and unsolvability detection. Through the analysis of usability, this technique could improve the modeling process directly, and we have demonstrated this capability through the tight integration with the online PDDL editor at Planning.Domains.}\\n}\\n\\n\",\"author_short\":[\"Zhang, Q.\",\"Muise, C.\"],\"key\":\"zhang-keps-2020\",\"id\":\"zhang-keps-2020\",\"bibbaseid\":\"zhang-muise-actionusabilityviadeadenddetection-2020\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"firstnames\":[\"Salomon\"],\"propositions\":[],\"lastnames\":[\"Wollenstein-Betech\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Christos\",\"G.\"],\"propositions\":[],\"lastnames\":[\"Cassandrass\"],\"suffixes\":[]},{\"firstnames\":[\"Ioannis\",\"Ch.\"],\"propositions\":[],\"lastnames\":[\"Paschalidis\"],\"suffixes\":[]},{\"firstnames\":[\"Yasaman\"],\"propositions\":[],\"lastnames\":[\"Khazaeni\"],\"suffixes\":[]}],\"title\":\"Explainability of Intelligent Transportation Systems using Knowledge Compilation: a Traffic Light Controller Case\",\"booktitle\":\"The 23rd IEEE International Conference on Intelligent Transportation Systems\",\"year\":\"2020\",\"url\":\"https://arxiv.org/pdf/2007.04916.pdf\",\"abstract\":\"Usage of automated controllers which make decisions on an environment are widespread and are often based on black-box models. We use Knowledge Compilation theory to bring explainability to the controllers decision given the state of the system. For this, we use simulated historical state action data as input and build a compact and structured representation which relates states with actions. We implement this method in a Traffic Light Control scenario where the controller selects the light cycle by observing the presence (or absence) of vehicles in different regions of the incoming roads.\",\"bibtex\":\"@inproceedings{betech-itsc-2020,\\n  author = {Salomon Wollenstein-Betech and Christian Muise and Christos G. Cassandrass and Ioannis Ch. Paschalidis and Yasaman Khazaeni},\\n  title = {Explainability of Intelligent Transportation Systems using Knowledge Compilation: a Traffic Light Controller Case},\\n  booktitle = {The 23rd IEEE International Conference on Intelligent Transportation Systems},\\n  year = {2020},\\n  url = {https://arxiv.org/pdf/2007.04916.pdf},\\n  abstract = {Usage of automated controllers which make decisions on an environment are widespread and are often based on black-box models. We use Knowledge Compilation theory to bring explainability to the controllers decision given the state of the system. For this, we use simulated historical state action data as input and build a compact and structured representation which relates states with actions. We implement this method in a Traffic Light Control scenario where the controller selects the light cycle by observing the presence (or absence) of vehicles in different regions of the incoming roads.},\\n}\\n\\n\",\"author_short\":[\"Wollenstein-Betech, S.\",\"Muise, C.\",\"Cassandrass, C. G.\",\"Paschalidis, I. C.\",\"Khazaeni, Y.\"],\"key\":\"betech-itsc-2020\",\"id\":\"betech-itsc-2020\",\"bibbaseid\":\"wollensteinbetech-muise-cassandrass-paschalidis-khazaeni-explainabilityofintelligenttransportationsystemsusingknowledgecompilationatrafficlightcontrollercase-2020\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://arxiv.org/pdf/2007.04916.pdf\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":11,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"firstnames\":[\"Masataro\"],\"propositions\":[],\"lastnames\":[\"Asai\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]}],\"title\":\"Learning Neural-Symbolic Descriptive Planning Models via Cube-Space Priors: The Voyage Home (to STRIPS)\",\"booktitle\":\"29th International Joint Conference on Artificial Intelligence\",\"url\":\"https://arxiv.org/pdf/2004.12850.pdf\",\"abstract\":\"We achieved a new milestone in the difficult task of enabling agents to learn about their environment autonomously. Our neuro-symbolic architecture is trained end-to-end to produce a succinct and effective discrete state transition model from images alone. Our target representation (the Planning Domain Definition Language) is already in a form that off-the-shelf solvers can consume, and opens the door to the rich array of modern heuristic search capabilities. We demonstrate how the sophisticated innate prior we place on the learning process significantly reduces the complexity of the learned representation, and reveals a connection to the graphtheoretic notion of cube-like graphs, thus opening the door to a deeper understanding of the ideal properties for learned symbolic representations. We show that the powerful domain-independent heuristics allow our system to solve visual 15-Puzzle instances which are beyond the reach of blind search, without resorting to the Reinforcement Learning approach that requires a huge amount of training on the domain-dependent reward information.\",\"year\":\"2020\",\"bibtex\":\"@inproceedings{asai-ijcai-2020,\\n  author = {Masataro Asai and Christian Muise},\\n  title = {Learning Neural-Symbolic Descriptive Planning Models via Cube-Space Priors: The Voyage Home (to STRIPS)},\\n  booktitle = {29th International Joint Conference on Artificial Intelligence},\\n  url = {https://arxiv.org/pdf/2004.12850.pdf},\\n  abstract = {We achieved a new milestone in the difficult task of enabling agents to learn about their environment autonomously. Our neuro-symbolic architecture is trained end-to-end to produce a succinct and effective discrete state transition model from images alone. Our target representation (the Planning Domain Definition Language) is already in a form that off-the-shelf solvers can consume, and opens the door to the rich array of modern heuristic search capabilities. We demonstrate how the sophisticated innate prior we place on the learning process significantly reduces the complexity of the learned representation, and reveals a connection to the graphtheoretic notion of cube-like graphs, thus opening the door to a deeper understanding of the ideal properties for learned symbolic representations. We show that the powerful domain-independent heuristics allow our system to solve visual 15-Puzzle instances which are beyond the reach of blind search, without resorting to the Reinforcement Learning approach that requires a huge amount of training on the domain-dependent reward information.},\\n  year = {2020},\\n}\\n\\n\",\"author_short\":[\"Asai, M.\",\"Muise, C.\"],\"key\":\"asai-ijcai-2020\",\"id\":\"asai-ijcai-2020\",\"bibbaseid\":\"asai-muise-learningneuralsymbolicdescriptiveplanningmodelsviacubespacepriorsthevoyagehometostrips-2020\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://arxiv.org/pdf/2004.12850.pdf\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":21,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"firstnames\":[\"Sarath\"],\"propositions\":[],\"lastnames\":[\"Sreedharan\"],\"suffixes\":[]},{\"firstnames\":[\"Tathagata\"],\"propositions\":[],\"lastnames\":[\"Chakraborti\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Yasaman\"],\"propositions\":[],\"lastnames\":[\"Khazaeni\"],\"suffixes\":[]},{\"firstnames\":[\"Subbarao\"],\"propositions\":[],\"lastnames\":[\"Kambhampati\"],\"suffixes\":[]}],\"title\":\"D3WA+: A Case Study of XAIP in a Model Acquisition Task\",\"booktitle\":\"Proceedings of the Thirtieth International Conference on Automated Planning and Scheduling (ICAPS)\",\"year\":\"2020\",\"abstract\":\"Recently, the D3WA system was proposed as a paradigm shift in how complex goal-oriented dialogue agents can be specified by taking a declarative view of design. However, it turns out actual users of the system have a hard time evolving their mental model and grasping the imperative consequences of declarative design. In this paper, we adopt ideas from existing works in the field of Explainable AI Planning (XAIP) to provide guidance to the dialogue designer during the model acquisition process. We will highlight in the course of this discussion how the setting presents unique challenges to the XAIP setting, including having to deal with a different user persona as the domain modeler rather than the end-user of the system, and consequently having to deal with the unsolvability of models in addition to explaining generated plans.\",\"bibtex\":\"@inproceedings{sreedharan-icaps20,\\n  author    = {Sarath Sreedharan and Tathagata Chakraborti and Christian Muise and Yasaman Khazaeni and Subbarao Kambhampati},\\n  title     = {{D3WA+}: A Case Study of {XAIP} in a Model Acquisition Task},\\n  booktitle = {Proceedings of the Thirtieth International Conference on Automated Planning and Scheduling ({ICAPS})},\\n  year      = {2020},\\n  abstract  = {Recently, the D3WA system was proposed as a paradigm shift in how complex goal-oriented dialogue agents can be specified by taking a declarative view of design. However, it turns out actual users of the system have a hard time evolving their mental model and grasping the imperative consequences of declarative design. In this paper, we adopt ideas from existing works in the field of Explainable AI Planning (XAIP) to provide guidance to the dialogue designer during the model acquisition process. We will highlight in the course of this discussion how the setting presents unique challenges to the XAIP setting, including having to deal with a different user persona as the domain modeler rather than the end-user of the system, and consequently having to deal with the unsolvability of models in addition to explaining generated plans.}\\n}\\n\\n\\n\",\"author_short\":[\"Sreedharan, S.\",\"Chakraborti, T.\",\"Muise, C.\",\"Khazaeni, Y.\",\"Kambhampati, S.\"],\"key\":\"sreedharan-icaps20\",\"id\":\"sreedharan-icaps20\",\"bibbaseid\":\"sreedharan-chakraborti-muise-khazaeni-kambhampati-d3waacasestudyofxaipinamodelacquisitiontask-2020\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"misc\",\"type\":\"misc\",\"title\":\"Planning for Goal-Oriented Dialogue Systems\",\"author\":[{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Tathagata\"],\"propositions\":[],\"lastnames\":[\"Chakraborti\"],\"suffixes\":[]},{\"firstnames\":[\"Shubham\"],\"propositions\":[],\"lastnames\":[\"Agarwal\"],\"suffixes\":[]},{\"firstnames\":[\"Ondrej\"],\"propositions\":[],\"lastnames\":[\"Bajgar\"],\"suffixes\":[]},{\"firstnames\":[\"Arunima\"],\"propositions\":[],\"lastnames\":[\"Chaudhary\"],\"suffixes\":[]},{\"firstnames\":[\"Luis\",\"A.\"],\"propositions\":[],\"lastnames\":[\"Lastras-Montano\"],\"suffixes\":[]},{\"firstnames\":[\"Josef\"],\"propositions\":[],\"lastnames\":[\"Ondrej\"],\"suffixes\":[]},{\"firstnames\":[\"Miroslav\"],\"propositions\":[],\"lastnames\":[\"Vodolan\"],\"suffixes\":[]},{\"firstnames\":[\"Charlie\"],\"propositions\":[],\"lastnames\":[\"Wiecha\"],\"suffixes\":[]}],\"year\":\"2019\",\"eprint\":\"1910.08137\",\"archiveprefix\":\"arXiv\",\"primaryclass\":\"cs.AI\",\"url\":\"https://arxiv.org/pdf/1910.08137\",\"abstract\":\"Generating complex multi-turn goal-oriented dialogue agents is a difficult problem that has seen a considerable focus from many leaders in the tech industry, including IBM, Google, Amazon, and Microsoft. This is in large part due to the rapidly growing market demand for dialogue agents capable of goal-oriented behaviour. Due to the business process nature of these conversations, end-to-end machine learning systems are generally not a viable option, as the generated dialogue agents must be deployable and verifiable on behalf of the businesses authoring them. In this work, we propose a paradigm shift in the creation of goal-oriented complex dialogue systems that dramatically eliminates the need for a designer to manually specify a dialogue tree, which nearly all current systems have to resort to when the interaction pattern falls outside standard patterns such as slot filling. We propose a declarative representation of the dialogue agent to be processed by state-of-the-art planning technology. Our proposed approach covers all aspects of the process; from model solicitation to the execution of the generated plans/dialogue agents. Along the way, we introduce novel planning encodings for declarative dialogue synthesis, a variety of interfaces for working with the specification as a dialogue architect, and a robust executor for generalized contingent plans. We have created prototype implementations of all components, and in this paper, we further demonstrate the resulting system empirically.\",\"bibtex\":\"@misc{muise2019planning,\\n    title={Planning for Goal-Oriented Dialogue Systems},\\n    author={Christian Muise and Tathagata Chakraborti and Shubham Agarwal and Ondrej Bajgar and Arunima Chaudhary and Luis A. Lastras-Montano and Josef Ondrej and Miroslav Vodolan and Charlie Wiecha},\\n    year={2019},\\n    eprint={1910.08137},\\n    archivePrefix={arXiv},\\n    primaryClass={cs.AI},\\n    url={https://arxiv.org/pdf/1910.08137},\\n    abstract={Generating complex multi-turn goal-oriented dialogue agents is a difficult problem that has seen a considerable focus from many leaders in the tech industry, including IBM, Google, Amazon, and Microsoft. This is in large part due to the rapidly growing market demand for dialogue agents capable of goal-oriented behaviour. Due to the business process nature of these conversations, end-to-end machine learning systems are generally not a viable option, as the generated dialogue agents must be deployable and verifiable on behalf of the businesses authoring them. In this work, we propose a paradigm shift in the creation of goal-oriented complex dialogue systems that dramatically eliminates the need for a designer to manually specify a dialogue tree, which nearly all current systems have to resort to when the interaction pattern falls outside standard patterns such as slot filling. We propose a declarative representation of the dialogue agent to be processed by state-of-the-art planning technology. Our proposed approach covers all aspects of the process; from model solicitation to the execution of the generated plans/dialogue agents. Along the way, we introduce novel planning encodings for declarative dialogue synthesis, a variety of interfaces for working with the specification as a dialogue architect, and a robust executor for generalized contingent plans. We have created prototype implementations of all components, and in this paper, we further demonstrate the resulting system empirically.}\\n}\\n\\n\\n\\n\\n\",\"author_short\":[\"Muise, C.\",\"Chakraborti, T.\",\"Agarwal, S.\",\"Bajgar, O.\",\"Chaudhary, A.\",\"Lastras-Montano, L. A.\",\"Ondrej, J.\",\"Vodolan, M.\",\"Wiecha, C.\"],\"key\":\"muise2019planning\",\"id\":\"muise2019planning\",\"bibbaseid\":\"muise-chakraborti-agarwal-bajgar-chaudhary-lastrasmontano-ondrej-vodolan-etal-planningforgoalorienteddialoguesystems-2019\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://arxiv.org/pdf/1910.08137\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":47,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Modeling Blackbox Agent Behaviour via Knowledge Compilation\",\"author\":[{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Salomón\"],\"propositions\":[],\"lastnames\":[\"Wollenstein-Betech\"],\"suffixes\":[]},{\"firstnames\":[\"Serena\"],\"propositions\":[],\"lastnames\":[\"Booth\"],\"suffixes\":[]},{\"firstnames\":[\"Julie\"],\"propositions\":[],\"lastnames\":[\"Shah\"],\"suffixes\":[]},{\"firstnames\":[\"Yasaman\"],\"propositions\":[],\"lastnames\":[\"Khazaeni\"],\"suffixes\":[]}],\"booktitle\":\"The AAAI 2020 Workshop on Plan, Activity, and Intent Recognition\",\"year\":\"2020\",\"abstract\":\"Understanding how agents behave in an environment is a corner-stone for interpretable AI. In this work, we focus on capturing the policy an agent is following without placing any assumptions on how that policy is actually implemented. From a corpus of state-action pairs, our task is to build a compact and diagnosable representation of the mapping from states to actions. We appeal to modern knowledge compilation techniques for this task and demonstrate empirically how this approach outperforms the previous state of the art. We further create an interactive inference on the compiled representation to get an intuitive sense of the policy.\",\"bibtex\":\"@inproceedings{muise-pair20,\\n  title={Modeling Blackbox Agent Behaviour via Knowledge Compilation},\\n  author={Christian Muise and Salomón Wollenstein-Betech and Serena Booth and Julie Shah and Yasaman Khazaeni},\\n  booktitle={The AAAI 2020 Workshop on Plan, Activity, and Intent Recognition},\\n  year={2020},\\n  abstract={Understanding how agents behave in an environment is a corner-stone for interpretable AI. In this work, we focus on capturing the policy an agent is following without placing any assumptions on how that policy is actually implemented. From a corpus of state-action pairs, our task is to build a compact and diagnosable representation of the mapping from states to actions. We appeal to modern knowledge compilation techniques for this task and demonstrate empirically how this approach outperforms the previous state of the art. We further create an interactive inference on the compiled representation to get an intuitive sense of the policy.}\\n}\\n\\n\",\"author_short\":[\"Muise, C.\",\"Wollenstein-Betech, S.\",\"Booth, S.\",\"Shah, J.\",\"Khazaeni, Y.\"],\"key\":\"muise-pair20\",\"id\":\"muise-pair20\",\"bibbaseid\":\"muise-wollensteinbetech-booth-shah-khazaeni-modelingblackboxagentbehaviourviaknowledgecompilation-2020\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Expectation-Aware Planning: A General Framework for Synthesizing and Executing Self-Explaining Plans for Human-AI Interaction\",\"author\":[{\"firstnames\":[\"Sarath\"],\"propositions\":[],\"lastnames\":[\"Sreedharan\"],\"suffixes\":[]},{\"firstnames\":[\"Tathagata\"],\"propositions\":[],\"lastnames\":[\"Chakraborti\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Subbarao\"],\"propositions\":[],\"lastnames\":[\"Kambhampati\"],\"suffixes\":[]}],\"booktitle\":\"The 34th AAAI Conference on Artificial Intelligence\",\"year\":\"2020\",\"abstract\":\"In this work, we present a new planning formalism called Expectation-Aware planning for decision making with humans in the loop where the human's expectations about an agent may differ from the agent's own model. We show how this formulation allows agents to not only leverage existing strategies for handling model differences like explanations and explicability, but can also exhibit novel behaviors that are generated through the combination of these different strategies. Our formulation also reveals a deep connection to existing approaches in epistemic planning. Specifically, we show how we can leverage classical planning compilations for epistemic planning to solve Expectation-Aware planning problems. To the best of our knowledge, the proposed formulation is the first complete solution to decision-making in the presence of diverging user expectations that is amenable to a classical planning compilation while successfully combining previous works on explanation and explicability. We empirically show how our approach provides a computational advantage over our earlier approaches that rely on search in the space of models.\",\"bibtex\":\"@inproceedings{sreedharan-aaai20,\\n  title={Expectation-Aware Planning: A General Framework for Synthesizing and Executing Self-Explaining Plans for Human-AI Interaction},\\n  author={Sarath Sreedharan and Tathagata Chakraborti and Christian Muise and Subbarao Kambhampati},\\n  booktitle={The 34th AAAI Conference on Artificial Intelligence},\\n  year={2020},\\n  abstract={In this work, we present a new planning formalism called Expectation-Aware planning for decision making with humans in the loop where the human's expectations about an agent may differ from the agent's own model. We show how this formulation allows agents to not only leverage existing strategies for handling model differences like explanations and explicability, but can also exhibit novel behaviors that are generated through the combination of these different strategies. Our formulation also reveals a deep connection to existing approaches in epistemic planning. Specifically, we show how we can leverage classical planning compilations for epistemic planning to solve Expectation-Aware planning problems. To the best of our knowledge, the proposed formulation is the first complete solution to decision-making in the presence of diverging user expectations that is amenable to a classical planning compilation while successfully combining previous works on explanation and explicability. We empirically show how our approach provides a computational advantage over our earlier approaches that rely on search in the space of models.}\\n}\\n\\n\",\"author_short\":[\"Sreedharan, S.\",\"Chakraborti, T.\",\"Muise, C.\",\"Kambhampati, S.\"],\"key\":\"sreedharan-aaai20\",\"id\":\"sreedharan-aaai20\",\"bibbaseid\":\"sreedharan-chakraborti-muise-kambhampati-expectationawareplanningageneralframeworkforsynthesizingandexecutingselfexplainingplansforhumanaiinteraction-2020\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"firstnames\":[\"Shubham\"],\"propositions\":[],\"lastnames\":[\"Agarwal\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Mayank\"],\"propositions\":[],\"lastnames\":[\"Agarwal\"],\"suffixes\":[]},{\"firstnames\":[\"Sohini\"],\"propositions\":[],\"lastnames\":[\"Upadhyay\"],\"suffixes\":[]},{\"firstnames\":[\"Zilu\"],\"propositions\":[],\"lastnames\":[\"Tang\"],\"suffixes\":[]},{\"firstnames\":[\"Zhongshen\"],\"propositions\":[],\"lastnames\":[\"Zeng\"],\"suffixes\":[]},{\"firstnames\":[\"Yasaman\"],\"propositions\":[],\"lastnames\":[\"Khazaeni\"],\"suffixes\":[]}],\"title\":\"TraceHub – A Platform to bridge the gap between State-Of-The-Art Time-Series Analytics and Datasets\",\"booktitle\":\"AAAI 2020 System Demonstration Track\",\"year\":\"2020\",\"bibbase_note\":\"<span style=\\\"color: purple\\\">Best System Demonstration Award.</span>\",\"abstract\":\"In this paper, we present TraceHub - a platform that connects new non-trivial state-of-the-art time-series analytics with datasets from different domains. Analytics owners can run their insights on new datasets in an automated setting to find insight's potential and improve it. Dataset owners can find all possible types of non-trivial insights based on latest research. We provide a plug-n-play system as a set of Dataset, Transformer pipeline, and Analytics APIs for both kinds of users. We show a usefulness measure of generated insights across various types of analytics in the system. We believe that this platform can be used to bridge the gap between time-series analytics and datasets by significantly reducing the time to find the true potential of budding time-series research and improving on it faster.\",\"bibtex\":\"@inproceedings{agarwal-aaaidemo20,\\n  author={Shubham Agarwal and Christian Muise and Mayank Agarwal and Sohini Upadhyay and Zilu Tang and Zhongshen Zeng and Yasaman Khazaeni},\\n  title={TraceHub -- A Platform to bridge the gap between State-Of-The-Art Time-Series Analytics and Datasets},\\n  booktitle={AAAI 2020 System Demonstration Track},\\n  year={2020},\\n  bibbase_note = {<span style=\\\"color: purple\\\">Best System Demonstration Award.</span>},\\n  abstract={In this paper, we present TraceHub - a platform that connects new non-trivial state-of-the-art time-series analytics with datasets from different domains. Analytics owners can run their insights on new datasets in an automated setting to find insight's potential and improve it. Dataset owners can find all possible types of non-trivial insights based on latest research. We provide a plug-n-play system as a set of Dataset, Transformer pipeline, and Analytics APIs for both kinds of users. We show a usefulness measure of generated insights across various types of analytics in the system. We believe that this platform can be used to bridge the gap between time-series analytics and datasets by significantly reducing the time to find the true potential of budding time-series research and improving on it faster.}\\n}\\n\\n\\n\",\"author_short\":[\"Agarwal, S.\",\"Muise, C.\",\"Agarwal, M.\",\"Upadhyay, S.\",\"Tang, Z.\",\"Zeng, Z.\",\"Khazaeni, Y.\"],\"key\":\"agarwal-aaaidemo20\",\"id\":\"agarwal-aaaidemo20\",\"bibbaseid\":\"agarwal-muise-agarwal-upadhyay-tang-zeng-khazaeni-tracehubaplatformtobridgethegapbetweenstateofthearttimeseriesanalyticsanddatasets-2020\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Towards Automated AI Planning for Enterprise Services: Opportunities and Challenges\",\"author\":[{\"firstnames\":[\"Maja\"],\"propositions\":[],\"lastnames\":[\"Vukovic\"],\"suffixes\":[]},{\"firstnames\":[\"Scott\"],\"propositions\":[],\"lastnames\":[\"Gerard\"],\"suffixes\":[]},{\"firstnames\":[\"Michael\"],\"propositions\":[],\"lastnames\":[\"Katz\"],\"suffixes\":[]},{\"firstnames\":[\"Laura\"],\"propositions\":[],\"lastnames\":[\"Shwartz\"],\"suffixes\":[]},{\"firstnames\":[\"Shirin\"],\"propositions\":[],\"lastnames\":[\"Sohrabi\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"John\"],\"propositions\":[],\"lastnames\":[\"Rofrano\"],\"suffixes\":[]},{\"firstnames\":[\"Anup\"],\"propositions\":[],\"lastnames\":[\"Kalia\"],\"suffixes\":[]},{\"firstnames\":[\"Jinho\"],\"propositions\":[],\"lastnames\":[\"Hwang\"],\"suffixes\":[]},{\"firstnames\":[\"Ya\",\"Bin\"],\"propositions\":[],\"lastnames\":[\"Dang\"],\"suffixes\":[]},{\"firstnames\":[\"Jie\",\"DJ\"],\"propositions\":[],\"lastnames\":[\"Ma\"],\"suffixes\":[]},{\"firstnames\":[\"Zhuo\",\"Xuan\"],\"propositions\":[],\"lastnames\":[\"Jiang\"],\"suffixes\":[]}],\"booktitle\":\"The 17th International Conference on Service-Oriented Computing (ICSOC-2019)\",\"year\":\"2019\",\"url\":\"https://link.springer.com/chapter/10.1007%2F978-3-030-33702-5_6\",\"abstract\":\"Existing Artificial Intelligence (AI) driven automation solutions in enterprises employ machine learning, natural language processing, and chatbots. There is an opportunity for AI Planning to be applied, which offers reasoning about action trajectories to help build automation blueprints. AI Planning is a problem-solving technique, where knowledge about available actions and their consequences is used to identify a sequence of actions, which, when applied in a given initial state, satisfy a desired goal. AI Planning has successfully been applied in a number of domains ranging from space applications, logistics and transportation, manufacturing, robotics, scheduling, e-learning, enterprise risk management, and service composition. In this paper, we discuss experience in building automation solutions that employ AI planning for use in enterprise IT and business services, such as change and event management, migration and transformation and RPA composition. We discuss challenges in adoption of AI planning across the enterprise from implementation and deployment perspectives.\",\"bibtex\":\"@inproceedings{vukovic-icsoc19,\\n  title={Towards Automated AI Planning for Enterprise Services: Opportunities and Challenges},\\n  author={Maja Vukovic and Scott Gerard and Michael Katz and Laura Shwartz and Shirin Sohrabi and Christian Muise and John Rofrano and Anup Kalia and Jinho Hwang and Ya Bin Dang and Jie DJ Ma and Zhuo Xuan Jiang},\\n  booktitle={The 17th International Conference on Service-Oriented Computing (ICSOC-2019)},\\n  year={2019},\\n  url={https://link.springer.com/chapter/10.1007%2F978-3-030-33702-5_6},\\n  abstract={Existing Artificial Intelligence (AI) driven automation solutions in enterprises employ machine learning, natural language processing, and chatbots. There is an opportunity for AI Planning to be applied, which offers reasoning about action trajectories to help build automation blueprints. AI Planning is a problem-solving technique, where knowledge about available actions and their consequences is used to identify a sequence of actions, which, when applied in a given initial state, satisfy a desired goal. AI Planning has successfully been applied in a number of domains ranging from space applications, logistics and transportation, manufacturing, robotics, scheduling, e-learning, enterprise risk management, and service composition. In this paper, we discuss experience in building automation solutions that employ AI planning for use in enterprise IT and business services, such as change and event management, migration and transformation and RPA composition. We discuss challenges in adoption of AI planning across the enterprise from implementation and deployment perspectives.}\\n}\\n\\n\",\"author_short\":[\"Vukovic, M.\",\"Gerard, S.\",\"Katz, M.\",\"Shwartz, L.\",\"Sohrabi, S.\",\"Muise, C.\",\"Rofrano, J.\",\"Kalia, A.\",\"Hwang, J.\",\"Dang, Y. B.\",\"Ma, J. D.\",\"Jiang, Z. X.\"],\"key\":\"vukovic-icsoc19\",\"id\":\"vukovic-icsoc19\",\"bibbaseid\":\"vukovic-gerard-katz-shwartz-sohrabi-muise-rofrano-kalia-etal-towardsautomatedaiplanningforenterpriseservicesopportunitiesandchallenges-2019\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://link.springer.com/chapter/10.1007%2F978-3-030-33702-5_6\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":3,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Bayesian Inference of Temporal Specifications to Explain How Plans Differ\",\"author\":[{\"firstnames\":[\"Joseph\"],\"propositions\":[],\"lastnames\":[\"Kim\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Ankit\"],\"propositions\":[],\"lastnames\":[\"Shah\"],\"suffixes\":[]},{\"firstnames\":[\"Shubham\"],\"propositions\":[],\"lastnames\":[\"Agarwal\"],\"suffixes\":[]},{\"firstnames\":[\"Julie\"],\"propositions\":[],\"lastnames\":[\"Shah\"],\"suffixes\":[]}],\"booktitle\":\"The 28th International Joint Conference on Artificial Intelligence (IJCAI)\",\"year\":\"2019\",\"url\":\"https://www.ijcai.org/proceedings/2019/0776.pdf\",\"abstract\":\"Temporal logics are useful for describing dynamic system behavior, and have been successfully used as a language for goal definitions during task planning. Prior works on inferring temporal logic specifications have focused on ``summarizing'' the input dataset – i.e., finding specifications that are satisfied by all plan traces belonging to the given set. In this paper, we examine the problem of inferring specifications that describe temporal differences between two sets of plan traces. We formalize the concept of providing such \\\\emphcontrastive explanations, then present a Bayesian probabilistic model for inferring contrastive explanations as linear temporal logic specifications. We demonstrate the efficacy, scalability, and robustness of our model for inferring correct specifications across various benchmark planning domains and for a simulated air combat mission.\",\"bibtex\":\"@inproceedings{kim-ijcai19,\\n  title={Bayesian Inference of Temporal Specifications to Explain How Plans Differ},\\n  author={Joseph Kim and Christian Muise and Ankit Shah and Shubham Agarwal and Julie Shah},\\n  booktitle={The 28th International Joint Conference on Artificial Intelligence (IJCAI)},\\n  year={2019},\\n  url={https://www.ijcai.org/proceedings/2019/0776.pdf},\\n  abstract={Temporal logics are useful for describing dynamic system behavior, and have been successfully used as a language for goal definitions during task planning. Prior works on inferring temporal logic specifications have focused on ``summarizing'' the input dataset -- i.e., finding specifications that are satisfied by all plan traces belonging to the given set. In this paper, we examine the problem of inferring specifications that describe temporal differences between two sets of plan traces. We formalize the concept of providing such \\\\emph{contrastive} explanations, then present a Bayesian probabilistic model for inferring contrastive explanations as linear temporal logic specifications. We demonstrate the efficacy, scalability, and robustness of our model for inferring correct specifications across various benchmark planning domains and for a simulated air combat mission.}\\n}\\n\\n\",\"author_short\":[\"Kim, J.\",\"Muise, C.\",\"Shah, A.\",\"Agarwal, S.\",\"Shah, J.\"],\"key\":\"kim-ijcai19\",\"id\":\"kim-ijcai19\",\"bibbaseid\":\"kim-muise-shah-agarwal-shah-bayesianinferenceoftemporalspecificationstoexplainhowplansdiffer-2019\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.ijcai.org/proceedings/2019/0776.pdf\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":21,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Evaluating the Interpretability of the Knowledge Compilation Map: Communicating Logical Statements Effectively\",\"author\":[{\"firstnames\":[\"Serena\"],\"propositions\":[],\"lastnames\":[\"Booth\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Julie\"],\"propositions\":[],\"lastnames\":[\"Shah\"],\"suffixes\":[]}],\"booktitle\":\"The 28th International Joint Conference on Artificial Intelligence (IJCAI)\",\"year\":\"2019\",\"url\":\"https://www.ijcai.org/proceedings/2019/0804.pdf\",\"abstract\":\"Knowledge compilation techniques translate propositional theories into equivalent forms to increase their computational tractability. But, how should we best present these propositional theories to a human? We analyze the standard taxonomy of propositional theories for relative \\\\emphinterpretability across three model domains: highway driving, emergency triage, and the chopsticks game. We generate decision-making agents which produce logical explanations for their actions and apply knowledge compilation to these explanations. Then, we evaluate how quickly, accurately, and confidently users comprehend the generated explanations to make decisions. We find that domain, formula size, and negated logical connectives significantly affect comprehension while formula properties typically associated with interpretability are not strong predictors of human ability to comprehend the theory.\",\"bibtex\":\"@inproceedings{booth-ijcai19,\\n  title={Evaluating the Interpretability of the Knowledge Compilation Map: Communicating Logical Statements Effectively},\\n  author={Serena Booth and Christian Muise and Julie Shah},\\n  booktitle={The 28th International Joint Conference on Artificial Intelligence (IJCAI)},\\n  year={2019},\\n  url={https://www.ijcai.org/proceedings/2019/0804.pdf},\\n  abstract={Knowledge compilation techniques translate propositional theories into equivalent forms to increase their computational tractability. But, how should we best present these propositional theories to a human? We analyze the standard taxonomy of propositional theories for relative \\\\emph{interpretability} across three model domains: highway driving, emergency triage, and the chopsticks game. We generate decision-making agents which produce logical explanations for their actions and apply knowledge compilation to these explanations. Then, we evaluate how quickly, accurately, and confidently users comprehend the generated explanations to make decisions. We find that domain, formula size, and negated logical connectives significantly affect comprehension while formula properties typically associated with interpretability are not strong predictors of human ability to comprehend the theory.}\\n}\\n\\n\",\"author_short\":[\"Booth, S.\",\"Muise, C.\",\"Shah, J.\"],\"key\":\"booth-ijcai19\",\"id\":\"booth-ijcai19\",\"bibbaseid\":\"booth-muise-shah-evaluatingtheinterpretabilityoftheknowledgecompilationmapcommunicatinglogicalstatementseffectively-2019\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.ijcai.org/proceedings/2019/0804.pdf\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":4,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"A General Framework for Synthesizing and Executing Self-Explaining Plans for Human-AI Interaction\",\"author\":[{\"firstnames\":[\"Sarath\"],\"propositions\":[],\"lastnames\":[\"Sreedharan\"],\"suffixes\":[]},{\"firstnames\":[\"Tathagata\"],\"propositions\":[],\"lastnames\":[\"Chakraborti\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Subbarao\"],\"propositions\":[],\"lastnames\":[\"Kambhampati\"],\"suffixes\":[]}],\"booktitle\":\"The 2nd ICAPS Workshop on Explainable Planning (XAIP-2019)\",\"year\":\"2019\",\"url\":\"https://openreview.net/pdf?id=H1gyE6hm5V\",\"abstract\":\"In this work, we present a general formulation for decision making in human-in-the-loop planning problems where the human’s expectations about an autonomous agent may differ from the agent’s own model. We show how our formulation for such multi-model planning problems allows us to capture existing approaches to this problem and also be used to generate novel explanatory behaviors. Our formulation also reveals a deep connection between multi-model planning and epistemic planning and we show how we can leverage classical planning compilations designed for epistemic planning for solving multi-model planning problems. We empirically show how this new compilation provides a computational advantage over previous approaches that separate reasoning about model reconciliation and identifying the agent’s plan.\",\"bibtex\":\"@inproceedings{sreedharan-xaip19,\\n  title={A General Framework for Synthesizing and Executing Self-Explaining Plans for Human-AI Interaction},\\n  author={Sarath Sreedharan and Tathagata Chakraborti and Christian Muise and Subbarao Kambhampati},\\n  booktitle={The 2nd ICAPS Workshop on Explainable Planning (XAIP-2019)},\\n  year={2019},\\n  url={https://openreview.net/pdf?id=H1gyE6hm5V},\\n  abstract={In this work, we present a general formulation for decision making in human-in-the-loop planning problems where the human’s expectations about an autonomous agent may differ from the agent’s own model. We show how our formulation for such multi-model planning problems allows us to capture existing approaches to this problem and also be used to generate novel explanatory behaviors. Our formulation also reveals a deep connection between multi-model planning and epistemic planning and we show how we can leverage classical planning compilations designed for epistemic planning for solving multi-model planning problems. We empirically show how this new compilation provides a computational advantage over previous approaches that separate reasoning about model reconciliation and identifying the agent’s plan.}\\n}\\n\\n\",\"author_short\":[\"Sreedharan, S.\",\"Chakraborti, T.\",\"Muise, C.\",\"Kambhampati, S.\"],\"key\":\"sreedharan-xaip19\",\"id\":\"sreedharan-xaip19\",\"bibbaseid\":\"sreedharan-chakraborti-muise-kambhampati-ageneralframeworkforsynthesizingandexecutingselfexplainingplansforhumanaiinteraction-2019\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://openreview.net/pdf?id=H1gyE6hm5V\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Executing Contingent Plans: Addressing Challenges in Deploying Artificial Agents\",\"author\":[{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Miroslav\"],\"propositions\":[],\"lastnames\":[\"Vodolan\"],\"suffixes\":[]},{\"firstnames\":[\"Shubham\"],\"propositions\":[],\"lastnames\":[\"Agarwal\"],\"suffixes\":[]},{\"firstnames\":[\"Ondrej\"],\"propositions\":[],\"lastnames\":[\"Bajgar\"],\"suffixes\":[]},{\"firstnames\":[\"Luis\"],\"propositions\":[],\"lastnames\":[\"Lastras\"],\"suffixes\":[]},{\"firstnames\":[\"Josef\"],\"propositions\":[],\"lastnames\":[\"Ondrej\"],\"suffixes\":[]}],\"booktitle\":\"ICAPS Workshop on Integrating Planning, Acting, and Execution (IntEx)\",\"year\":\"2019\",\"url\":\"https://icaps19.icaps-conference.org/workshops/IntEx/IntEx-2019-proceedings.pdf#page=32\",\"abstract\":\"The vast majority of research in automated planning focuses on generating a plan from an initial problem specification; from the theoretical properties of this task to the implementation details required to do so efficiently. While such work is often motivated by practical applications, there is far less understanding of the issues associated with executing plans in online environments. In this work we focus on this understudied area, and the challenges / opportunities that arise when executing complex plans. Unlike many works in plan execution, we consider a form of contingent plans as the source for execution; their complexity stems from the sophisticated representation of the action effects used to model the uncertainty in the world. The key contribution of our work is a proposed executor that can reason using the sophisticated action effects, and we demonstrate the impact this can have empirically. In support of an effective executor, we also consider (1) the connection between the execution context and the planner's view of the state of the world; and (2) the separation between the execution of an action (the aspect that affects the outside environment) and the realization of its effects (the aspect that captures what has actually changed).\",\"bibtex\":\"@inproceedings{muise-intex19,\\n  title={Executing Contingent Plans: Addressing Challenges in Deploying Artificial Agents},\\n  author={Christian Muise and Miroslav Vodolan and Shubham Agarwal and Ondrej Bajgar and Luis Lastras and Josef Ondrej},\\n  booktitle={ICAPS Workshop on Integrating Planning, Acting, and Execution (IntEx)},\\n  year={2019},\\n  url = {https://icaps19.icaps-conference.org/workshops/IntEx/IntEx-2019-proceedings.pdf#page=32},\\n  abstract={The vast majority of research in automated planning focuses on generating a plan from an initial problem specification; from the theoretical properties of this task to the implementation details required to do so efficiently. While such work is often motivated by practical applications, there is far less understanding of the issues associated with executing plans in online environments. In this work we focus on this understudied area, and the challenges / opportunities that arise when executing complex plans. Unlike many works in plan execution, we consider a form of contingent plans as the source for execution; their complexity stems from the sophisticated representation of the action effects used to model the uncertainty in the world. The key contribution of our work is a proposed executor that can reason using the sophisticated action effects, and we demonstrate the impact this can have empirically. In support of an effective executor, we also consider (1) the connection between the execution context and the planner's view of the state of the world; and (2) the separation between the execution of an action (the aspect that affects the outside environment) and the realization of its effects (the aspect that captures what has actually changed).}\\n}\\n\\n\",\"author_short\":[\"Muise, C.\",\"Vodolan, M.\",\"Agarwal, S.\",\"Bajgar, O.\",\"Lastras, L.\",\"Ondrej, J.\"],\"key\":\"muise-intex19\",\"id\":\"muise-intex19\",\"bibbaseid\":\"muise-vodolan-agarwal-bajgar-lastras-ondrej-executingcontingentplansaddressingchallengesindeployingartificialagents-2019\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://icaps19.icaps-conference.org/workshops/IntEx/IntEx-2019-proceedings.pdf#page=32\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":3,\"html\":\"\"},{\"bibtype\":\"book\",\"type\":\"book\",\"author\":[{\"firstnames\":[\"Patrik\"],\"propositions\":[],\"lastnames\":[\"Haslum\"],\"suffixes\":[]},{\"firstnames\":[\"Nir\"],\"propositions\":[],\"lastnames\":[\"Lipovetzky\"],\"suffixes\":[]},{\"firstnames\":[\"Daniele\"],\"propositions\":[],\"lastnames\":[\"Magazzeni\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]}],\"title\":\"An Introduction to the Planning Domain Definition Language\",\"publisher\":\"Morgan & Claypool\",\"year\":\"2019\",\"isbn\":\"9781627058759\",\"url\":\"http://www.morganclaypoolpublishers.com/catalog_Orig/product_info.php?products_id=1384\",\"abstract\":\"Planning is the branch of Artificial Intelligence (AI) that seeks to automate reasoning about plans, most importantly the reasoning that goes into formulating a plan to achieve a given goal in a given situation. AI planning is model-based: a planning system takes as input a description (or model) of the initial situation, the actions available to change it, and the goal condition to output a plan composed of those actions that will accomplish the goal when executed from the initial situation. The Planning Domain Definition Language (PDDL) is a formal knowledge representation language designed to express planning models. Developed by the planning research community as a means of facilitating systems comparison, it has become a de-facto standard input language of many planning systems, although it is not the only modelling language for planning. Several variants of PDDL have emerged that capture planning problems of different natures and complexities, with a focus on deterministic problems. The purpose of this book is two-fold. First, we present a unified and current account of PDDL, covering the subsets of PDDL that express discrete, numeric, temporal, and hybrid planning. Second, we want to introduce readers to the art of modelling planning problems in this language, through educational examples that demonstrate how PDDL is used to model realistic planning problems. The book is intended for advanced students and researchers in AI who want to dive into the mechanics of AI planning, as well as those who want to be able to use AI planning systems without an in-depth explanation of the algorithms and implementation techniques they use.\",\"bibtex\":\"@book{pddlbook,\\n  author    = {Patrik Haslum and Nir Lipovetzky and Daniele Magazzeni and Christian Muise},\\n  title     = {An Introduction to the Planning Domain Definition Language},\\n  publisher = {Morgan \\\\& Claypool},\\n  year      = {2019},\\n  isbn      = {9781627058759},\\n  url       = {http://www.morganclaypoolpublishers.com/catalog_Orig/product_info.php?products_id=1384},\\n  abstract  = {Planning is the branch of Artificial Intelligence (AI) that seeks to automate reasoning about plans, most importantly the reasoning that goes into formulating a plan to achieve a given goal in a given situation. AI planning is model-based: a planning system takes as input a description (or model) of the initial situation, the actions available to change it, and the goal condition to output a plan composed of those actions that will accomplish the goal when executed from the initial situation.\\n\\nThe Planning Domain Definition Language (PDDL) is a formal knowledge representation language designed to express planning models. Developed by the planning research community as a means of facilitating systems comparison, it has become a de-facto standard input\\n\\nlanguage of many planning systems, although it is not the only modelling language for planning. Several variants of PDDL have emerged that capture planning problems of different natures and complexities, with a focus on deterministic problems. The purpose of this book is two-fold. First, we present a unified and current account of PDDL, covering the subsets of PDDL that express discrete, numeric, temporal, and hybrid planning. Second, we want to introduce readers to the art of modelling planning problems in this language, through educational examples that demonstrate how PDDL is used to model realistic planning problems. The book is intended for advanced students and researchers in AI who want to dive into the mechanics of AI planning, as well as those who want to be able to use AI planning systems without an in-depth explanation of the algorithms and implementation techniques they use.}\\n}\\n\\n\",\"author_short\":[\"Haslum, P.\",\"Lipovetzky, N.\",\"Magazzeni, D.\",\"Muise, C.\"],\"key\":\"pddlbook\",\"id\":\"pddlbook\",\"bibbaseid\":\"haslum-lipovetzky-magazzeni-muise-anintroductiontotheplanningdomaindefinitionlanguage-2019\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.morganclaypoolpublishers.com/catalog_Orig/product_info.php?products_id=1384\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":4,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"firstnames\":[\"Jan\"],\"propositions\":[],\"lastnames\":[\"Dolejsi\"],\"suffixes\":[]},{\"firstnames\":[\"Derek\"],\"propositions\":[],\"lastnames\":[\"Long\"],\"suffixes\":[]},{\"firstnames\":[\"Maria\"],\"propositions\":[],\"lastnames\":[\"Fox\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]}],\"title\":\"From a Classroom to an Industry From PDDL “Hello World” to Debugging a Planning Problem\",\"booktitle\":\"System Demonstrations at the Twenty-Ninth International Conference on Automated Planning and Scheduling (ICAPS)\",\"year\":\"2019\",\"url\":\"http://www.haz.ca/papers/icaps-demo-dolejsi-19.pdf\",\"abstract\":\"This demonstration will build on previously shown end-to-end PDDL2.2 developer environ-ment built on top of Microsoft’s VS Code, but will expand substantially in two directions. First, the session from editor.planning.domains can seam-lessly migrate from the web browser to VS Code editor and take advantage of more elaborate PDDL support. Second, VS Code now integrates debugging tools for PDDL modelers such as search tree visualization, step-by-step search de-bugger and plan validation. This offers coherent set of tools to use from classroom to an industrial planning application debugging.\",\"bibtex\":\"@inproceedings{pdeditor-icaps19demo,\\n  author    = {Jan Dolejsi and Derek Long and Maria Fox and Christian Muise},\\n  title     = {From a Classroom to an Industry From PDDL “Hello World” to Debugging a Planning Problem},\\n  booktitle = {System Demonstrations at the Twenty-Ninth International Conference on Automated Planning and Scheduling ({ICAPS})},\\n  year      = {2019},\\n  url       = {http://www.haz.ca/papers/icaps-demo-dolejsi-19.pdf},\\n  abstract  = {This demonstration will build on previously shown end-to-end PDDL2.2 developer environ-ment built on top of Microsoft’s VS Code, but will expand substantially in two directions. First, the session from editor.planning.domains can seam-lessly migrate from the web browser to VS Code editor and take advantage of more elaborate PDDL support. Second, VS Code now integrates debugging tools for PDDL modelers such as search tree visualization, step-by-step search de-bugger and plan validation. This offers coherent set of tools to use from classroom to an industrial planning application debugging.}\\n}\\n\\n\",\"author_short\":[\"Dolejsi, J.\",\"Long, D.\",\"Fox, M.\",\"Muise, C.\"],\"key\":\"pdeditor-icaps19demo\",\"id\":\"pdeditor-icaps19demo\",\"bibbaseid\":\"dolejsi-long-fox-muise-fromaclassroomtoanindustryfrompddlhelloworldtodebuggingaplanningproblem-2019\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.haz.ca/papers/icaps-demo-dolejsi-19.pdf\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"firstnames\":[\"Tathagata\"],\"propositions\":[],\"lastnames\":[\"Chakraborti\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Shubham\"],\"propositions\":[],\"lastnames\":[\"Agarwal\"],\"suffixes\":[]},{\"firstnames\":[\"Luis\",\"A.\"],\"propositions\":[],\"lastnames\":[\"Lastras\"],\"suffixes\":[]},{\"firstnames\":[\"Yasaman\"],\"propositions\":[],\"lastnames\":[\"Khazaeni\"],\"suffixes\":[]}],\"title\":\"MAi: An Interface for Declarative Specification of Goal-Directed Dialogue Agents\",\"booktitle\":\"System Demonstrations at the Twenty-Eighth International Conference on Automated Planning and Scheduling (ICAPS)\",\"year\":\"2019\",\"url\":\"http://www.haz.ca/papers/icaps-demo-chakraborti-19.pdf\",\"abstract\":\"The state of the art of dialogue agents requires a lengthy design process spanning months with experts in the loop who specify complex conversation patterns manually. Our work proposes a paradigm shift in bot design by adopting a declarative approach which composes the full dialog tree automatically. This allows the designer to construct complex dialogue agents from scratch and interact with them in a matter of hours. The demonstration will allow the audience to interact with this new design paradigm and construct their own bots on the spot.\",\"bibtex\":\"@inproceedings{mai-icaps19demo,\\n  author    = {Tathagata Chakraborti and Christian Muise and Shubham Agarwal and Luis A. Lastras and Yasaman Khazaeni},\\n  title     = {{MAi}: An Interface for Declarative Specification of Goal-Directed Dialogue Agents},\\n  booktitle = {System Demonstrations at the Twenty-Eighth International Conference on Automated Planning and Scheduling ({ICAPS})},\\n  year      = {2019},\\n  url       = {http://www.haz.ca/papers/icaps-demo-chakraborti-19.pdf},\\n  abstract  = {The state of the art of dialogue agents requires a lengthy design process spanning months with experts in the loop who specify complex conversation patterns manually. Our work proposes a paradigm shift in bot design by adopting a declarative approach which composes the full dialog tree automatically. This allows the designer to construct complex dialogue agents from scratch and interact with them in a matter of hours. The demonstration will allow the audience to interact with this new design paradigm and construct their own bots on the spot.}\\n}\\n\\n\",\"author_short\":[\"Chakraborti, T.\",\"Muise, C.\",\"Agarwal, S.\",\"Lastras, L. A.\",\"Khazaeni, Y.\"],\"key\":\"mai-icaps19demo\",\"id\":\"mai-icaps19demo\",\"bibbaseid\":\"chakraborti-muise-agarwal-lastras-khazaeni-maianinterfacefordeclarativespecificationofgoaldirecteddialogueagents-2019\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.haz.ca/papers/icaps-demo-chakraborti-19.pdf\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Generating Dialogue Agents via Automated Planning\",\"author\":[{\"firstnames\":[\"Adi\"],\"propositions\":[],\"lastnames\":[\"Botea\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Shubham\"],\"propositions\":[],\"lastnames\":[\"Agarwal\"],\"suffixes\":[]},{\"firstnames\":[\"Oznur\"],\"propositions\":[],\"lastnames\":[\"Alkan\"],\"suffixes\":[]},{\"firstnames\":[\"Ondrej\"],\"propositions\":[],\"lastnames\":[\"Bajgar\"],\"suffixes\":[]},{\"firstnames\":[\"Elizabeth\"],\"propositions\":[],\"lastnames\":[\"Daly\"],\"suffixes\":[]},{\"firstnames\":[\"Akihiro\"],\"propositions\":[],\"lastnames\":[\"Kishimoto\"],\"suffixes\":[]},{\"firstnames\":[\"Luis\"],\"propositions\":[],\"lastnames\":[\"Lastras\"],\"suffixes\":[]},{\"firstnames\":[\"Radu\"],\"propositions\":[],\"lastnames\":[\"Marinescu\"],\"suffixes\":[]},{\"firstnames\":[\"Josef\"],\"propositions\":[],\"lastnames\":[\"Ondrej\"],\"suffixes\":[]},{\"firstnames\":[\"Pablo\"],\"propositions\":[],\"lastnames\":[\"Pedemonte\"],\"suffixes\":[]},{\"firstnames\":[\"Miroslav\"],\"propositions\":[],\"lastnames\":[\"Vodolan\"],\"suffixes\":[]}],\"booktitle\":\"The Second AAAI Workshop On Reasoning And Learning For Human-Machine Dialogues (DEEP-DIAL 2019)\",\"year\":\"2019\",\"url\":\"http://www.haz.ca/papers/deepdial-botea-19.pdf\",\"abstract\":\"Dialogue systems have many applications such as customer support or question answering. Typically they have been limited to shallow single turn interactions. However more advanced applications such as career coaching or planning a trip require a much more complex multi-turn dialogue. Current limitations of conversational systems have made it difficult to support applications that require personalization, customization and context dependent interactions. We tackle this challenging problem by using domain-independent AI planning to automatically create dialogue plans, customized to guide a dialogue towards achieving a given goal. The input includes a library of atomic dialogue actions, an initial state of the dialogue, and a goal. Dialogue plans are plugged into a dialogue system capable to orchestrate their execution. Use cases demonstrate the viability of the approach. Our work on dialogue planning has been integrated into a product, and it is in the process of being deployed into another.\",\"bibtex\":\"@inproceedings{botea2019deepdial,\\n  title={Generating Dialogue Agents via Automated Planning},\\n  author={Adi Botea and Christian Muise and Shubham Agarwal and Oznur Alkan and Ondrej Bajgar and Elizabeth Daly and Akihiro Kishimoto and Luis Lastras and Radu Marinescu and Josef Ondrej and Pablo Pedemonte and Miroslav Vodolan},\\n  booktitle={The Second AAAI Workshop On Reasoning And Learning For Human-Machine Dialogues (DEEP-DIAL 2019)},\\n  year={2019},\\n  url={http://www.haz.ca/papers/deepdial-botea-19.pdf},\\n  abstract={Dialogue systems have many applications such as customer support or question answering. Typically they have been limited to shallow single turn interactions. However more advanced applications such as career coaching or planning a trip require a much more complex multi-turn dialogue. Current limitations of conversational systems have made it difficult to support applications that require personalization, customization and context dependent interactions. We tackle this challenging problem by using domain-independent AI planning to automatically create dialogue plans, customized to guide a dialogue towards achieving a given goal. The input includes a library of atomic dialogue actions, an initial state of the dialogue, and a goal. Dialogue plans are plugged into a dialogue system capable to orchestrate their execution. Use cases demonstrate the viability of the approach. Our work on dialogue planning has been integrated into a product, and it is in the process of being deployed into another.}\\n}\\n\\n\",\"author_short\":[\"Botea, A.\",\"Muise, C.\",\"Agarwal, S.\",\"Alkan, O.\",\"Bajgar, O.\",\"Daly, E.\",\"Kishimoto, A.\",\"Lastras, L.\",\"Marinescu, R.\",\"Ondrej, J.\",\"Pedemonte, P.\",\"Vodolan, M.\"],\"key\":\"botea2019deepdial\",\"id\":\"botea2019deepdial\",\"bibbaseid\":\"botea-muise-agarwal-alkan-bajgar-daly-kishimoto-lastras-etal-generatingdialogueagentsviaautomatedplanning-2019\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.haz.ca/papers/deepdial-botea-19.pdf\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"firstnames\":[\"Tathagata\"],\"propositions\":[],\"lastnames\":[\"Chakraborti\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Shubham\"],\"propositions\":[],\"lastnames\":[\"Agarwal\"],\"suffixes\":[]},{\"firstnames\":[\"Luis\",\"A.\"],\"propositions\":[],\"lastnames\":[\"Lastras\"],\"suffixes\":[]}],\"title\":\"MAi: An Intelligent Model Acquisition Interface for Interactive Specification of Dialog Agents\",\"booktitle\":\"AAAI 2019 System Demonstration Track\",\"year\":\"2019\",\"url\":\"http://www.haz.ca/papers/aaai-demo-chakraborti-19.pdf\",\"abstract\":\"The state of the art in automated conversational agents for enterprise (e.g. for customer support) require a lengthy design process with experts in the loop who have to figure out and specify complex conversation patterns. This demonstration looks at a prototype interface that aims to bring down the expertise required to design such agents as well as the time taken to do so. Specifically, we will focus on how a metawriter can assist the domain-writer during the design process and how complex conversation patterns can be derived from simplifying abstractions at the interface level.\",\"bibtex\":\"@inproceedings{mai-aaai19demo,\\n  author    = {Tathagata Chakraborti and Christian Muise and Shubham Agarwal and Luis A. Lastras},\\n  title     = {{MAi}: An Intelligent Model Acquisition Interface for Interactive Specification of Dialog Agents},\\n  booktitle = {AAAI 2019 System Demonstration Track},\\n  year      = {2019},\\n  url       = {http://www.haz.ca/papers/aaai-demo-chakraborti-19.pdf},\\n  abstract  = {The state of the art in automated conversational agents for enterprise (e.g. for customer support) require a lengthy design process with experts in the loop who have to figure out and specify complex conversation patterns. This demonstration looks at a prototype interface that aims to bring down the expertise required to design such agents as well as the time taken to do so. Specifically, we will focus on how a metawriter can assist the domain-writer during the design process and how complex conversation patterns can be derived from simplifying abstractions at the interface level.}\\n}\\n\\n\",\"author_short\":[\"Chakraborti, T.\",\"Muise, C.\",\"Agarwal, S.\",\"Lastras, L. A.\"],\"key\":\"mai-aaai19demo\",\"id\":\"mai-aaai19demo\",\"bibbaseid\":\"chakraborti-muise-agarwal-lastras-maianintelligentmodelacquisitioninterfaceforinteractivespecificationofdialogagents-2019\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.haz.ca/papers/aaai-demo-chakraborti-19.pdf\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Executing Contingent Plans: Challenges in Deploying Artificial Agents\",\"author\":[{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Miroslav\"],\"propositions\":[],\"lastnames\":[\"Vodolan\"],\"suffixes\":[]},{\"firstnames\":[\"Shubham\"],\"propositions\":[],\"lastnames\":[\"Agarwal\"],\"suffixes\":[]},{\"firstnames\":[\"Ondrej\"],\"propositions\":[],\"lastnames\":[\"Bajgar\"],\"suffixes\":[]},{\"firstnames\":[\"Luis\"],\"propositions\":[],\"lastnames\":[\"Lastras\"],\"suffixes\":[]}],\"booktitle\":\"Fall Symposium on Integrating Planning, Diagnosis, and Causal Reasoning\",\"year\":\"2018\",\"url\":\"http://www.haz.ca/papers/sip-muise-18.pdf\",\"abstract\":\"The vast majority of research in automated planning focuses on generating a plan from an initial problem specification, from the theoretical properties of this task to the implementation details required to do so efficiently. While such work is often motivated by practical applications, there is far less understanding of the issues associated with executing plans in online environments. In this work, we focus on some of the key challenges of executing contingent plans in a real-world deployment, and present some possible solutions.\",\"bibtex\":\"@inproceedings{muise2018sip,\\n  title={Executing Contingent Plans: Challenges in Deploying Artificial Agents},\\n  author={Christian Muise and Miroslav Vodolan and Shubham Agarwal and Ondrej Bajgar and Luis Lastras},\\n  booktitle={Fall Symposium on Integrating Planning, Diagnosis, and Causal Reasoning},\\n  year={2018},\\n  url={http://www.haz.ca/papers/sip-muise-18.pdf},\\n  abstract={The vast majority of research in automated planning focuses on generating a plan from an initial problem specification, from the theoretical properties of this task to the implementation details required to do so efficiently. While such work is often motivated by practical applications, there is far less understanding of the issues associated with executing plans in online environments. In this work, we focus on some of the key challenges of executing contingent plans in a real-world deployment, and present some possible solutions.}\\n}\\n\\n\",\"author_short\":[\"Muise, C.\",\"Vodolan, M.\",\"Agarwal, S.\",\"Bajgar, O.\",\"Lastras, L.\"],\"key\":\"muise2018sip\",\"id\":\"muise2018sip\",\"bibbaseid\":\"muise-vodolan-agarwal-bajgar-lastras-executingcontingentplanschallengesindeployingartificialagents-2018\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.haz.ca/papers/sip-muise-18.pdf\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Characterizing and Computing All Delete-Relaxed Dead-ends\",\"author\":[{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]}],\"booktitle\":\"Workshop on Constraint Satisfaction Techniques for Planning and Scheduling Problems (COPLAS'18)\",\"year\":\"2018\",\"url\":\"http://www.haz.ca/papers/coplas-muise-18.pdf\",\"abstract\":\"Dead-end detection is a key challenge in automated planning, and it is rapidly growing in popularity. Effective dead-end detection techniques can have a large impact on the strength of a planner, and so the effective computation of dead-ends is central to many planning approaches. One of the better understood techniques for detecting dead-ends is to focus on the delete relaxation of a planning problem, where dead-end detection is a polynomial-time operation. In this work, we provide a logical characterization for not just a single dead-end, but for every delete-relaxed dead-end in a planning problem. With a logical representation in hand, one could compile the representation into a form amenable to effective reasoning. We lay the ground-work for this larger vision and provide a preliminary evaluation to this end.\",\"bibtex\":\"@inproceedings{muise2018drde,\\n  title={Characterizing and Computing All Delete-Relaxed Dead-ends},\\n  author={Christian Muise},\\n  booktitle={Workshop on Constraint Satisfaction Techniques for Planning and Scheduling Problems (COPLAS'18)},\\n  year={2018},\\n  url={http://www.haz.ca/papers/coplas-muise-18.pdf},\\n  abstract={Dead-end detection is a key challenge in automated planning,\\nand it is rapidly growing in popularity. Effective dead-end\\ndetection techniques can have a large impact on the strength\\nof a planner, and so the effective computation of dead-ends is\\ncentral to many planning approaches. One of the better understood\\ntechniques for detecting dead-ends is to focus on the\\ndelete relaxation of a planning problem, where dead-end detection\\nis a polynomial-time operation. In this work, we provide\\na logical characterization for not just a single dead-end,\\nbut for every delete-relaxed dead-end in a planning problem.\\nWith a logical representation in hand, one could compile the\\nrepresentation into a form amenable to effective reasoning.\\nWe lay the ground-work for this larger vision and provide a\\npreliminary evaluation to this end.}\\n}\\n\\n\",\"author_short\":[\"Muise, C.\"],\"key\":\"muise2018drde\",\"id\":\"muise2018drde\",\"bibbaseid\":\"muise-characterizingandcomputingalldeleterelaxeddeadends-2018\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.haz.ca/papers/coplas-muise-18.pdf\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Non-Traditional Objective Functions for MDPs\",\"author\":[{\"firstnames\":[\"Sven\"],\"propositions\":[],\"lastnames\":[\"Koenig\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Scott\"],\"propositions\":[],\"lastnames\":[\"Sanner\"],\"suffixes\":[]}],\"booktitle\":\"6th Goal Reasoning Workshop\",\"year\":\"2018\",\"url\":\"http://www.haz.ca/papers/grw-koenig-18.pdf\",\"abstract\":\"While probabilistic planning in AI research has largely focused on cost-optimal goal-based objectives, we argue that many realistic planning problems require more complex objective functions and different perspectives on goals than is commonly found in the literature. In this paper, we try to understand the existing focus of probabilistic planning on cost-optimal goal-based objectives, then we proceed to outline some early and current AI research on non-traditional objectives. We conclude by charging AI researchers to focus more on realistic objectives to make probabilistic planning more attractive for actual applications.\",\"bibtex\":\"@inproceedings{koenig2018grw,\\n  title={Non-Traditional Objective Functions for MDPs},\\n  author={Sven Koenig and Christian Muise and Scott Sanner},\\n  booktitle={6th Goal Reasoning Workshop},\\n  year={2018},\\n  url={http://www.haz.ca/papers/grw-koenig-18.pdf},\\n  abstract={While probabilistic planning in AI research has\\nlargely focused on cost-optimal goal-based objectives,\\nwe argue that many realistic planning problems\\nrequire more complex objective functions and\\ndifferent perspectives on goals than is commonly\\nfound in the literature. In this paper, we try to understand\\nthe existing focus of probabilistic planning\\non cost-optimal goal-based objectives, then\\nwe proceed to outline some early and current AI research\\non non-traditional objectives. We conclude\\nby charging AI researchers to focus more on realistic\\nobjectives to make probabilistic planning more\\nattractive for actual applications.}\\n}\\n\\n\",\"author_short\":[\"Koenig, S.\",\"Muise, C.\",\"Sanner, S.\"],\"key\":\"koenig2018grw\",\"id\":\"koenig2018grw\",\"bibbaseid\":\"koenig-muise-sanner-nontraditionalobjectivefunctionsformdps-2018\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.haz.ca/papers/grw-koenig-18.pdf\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"firstnames\":[\"Nikhil\"],\"propositions\":[],\"lastnames\":[\"Bhargava\"],\"suffixes\":[]},{\"firstnames\":[\"Tiago\"],\"propositions\":[],\"lastnames\":[\"Vaquero\"],\"suffixes\":[]},{\"firstnames\":[\"Brian\"],\"propositions\":[],\"lastnames\":[\"Williams\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]}],\"title\":\"Managing Communication Costs under Temporal Uncertainty\",\"booktitle\":\"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI)\",\"year\":\"2018\",\"url\":\"https://www.ijcai.org/proceedings/2018/0648.pdf\",\"abstract\":\"In multi-agent temporal planning, individual agents cannot know a priori when other agents will execute their actions and so treat those actions as uncertain. Only when others communicate the results of their actions can that uncertainty be resolved. If a full communication protocol is specified ahead of time, then delay controllability can be used to assess the feasibility of the temporal plan. However, agents often have flexibility in choosing when to communicate the results of their action. In this paper, we address the question of how to choose communication protocols that guarantee the feasibility of the original temporal plan subject to some cost associated with that communication. To do so, we introduce a means of extracting delay controllability conflicts and show how we can use these conflicts to more efficiently guide our search. We then present three conflict-directed search algorithms and explore the theoretical and empirical tradeoffs between the different approaches.\",\"bibtex\":\"@inproceedings{bhar-vaq-wil-mui-ijcai18,\\n  author    = {Nikhil Bhargava and\\n               Tiago Vaquero and\\n               Brian Williams and\\n               Christian Muise},\\n  title     = {Managing Communication Costs under Temporal Uncertainty},\\n  booktitle = {Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ({IJCAI})},\\n  year      = {2018},\\n  url       = {https://www.ijcai.org/proceedings/2018/0648.pdf},\\n  abstract  = {In multi-agent temporal planning, individual agents cannot know a priori when other agents will execute their actions and so treat those actions as uncertain. Only when others communicate the results of their actions can that uncertainty be resolved. If a full communication protocol is specified ahead of time, then delay controllability can be used to assess the feasibility of the temporal plan. However, agents often have flexibility in choosing when to communicate the results of their action. In this paper, we address the question of how to choose communication protocols that guarantee the feasibility of the original temporal plan subject to some cost associated with that communication. To do so, we introduce a means of extracting delay controllability conflicts and show how we can use these conflicts to more efficiently guide our search. We then present three conflict-directed search algorithms and explore the theoretical and empirical tradeoffs between the different approaches.}\\n}\\n\\n\",\"author_short\":[\"Bhargava, N.\",\"Vaquero, T.\",\"Williams, B.\",\"Muise, C.\"],\"key\":\"bhar-vaq-wil-mui-ijcai18\",\"id\":\"bhar-vaq-wil-mui-ijcai18\",\"bibbaseid\":\"bhargava-vaquero-williams-muise-managingcommunicationcostsundertemporaluncertainty-2018\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.ijcai.org/proceedings/2018/0648.pdf\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"firstnames\":[\"Nikhil\"],\"propositions\":[],\"lastnames\":[\"Bhargava\"],\"suffixes\":[]},{\"firstnames\":[\"Brian\"],\"propositions\":[],\"lastnames\":[\"Williams\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]}],\"title\":\"Variable-Delay Controllability\",\"booktitle\":\"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI)\",\"year\":\"2018\",\"abstract\":\"In temporal planning, agents must schedule a set of events satisfying a set of predetermined constraints. These scheduling problems become more difficult when the duration of certain actions are outside the agent's control. Delay controllability is the generalized notion of whether a schedule can be constructed in the face of uncertainty if the agent eventually learns when events occur. Our work introduces the substantially more complex setting of determining variable-delay controllability, where an agent learns about events after some unknown but bounded amount of time has passed. We provide an efficient O(n^3) variable-delay controllability checker and show how to create an execution strategy for variable-delay controllability problems. We also provide preliminary empirical evaluations of the quality of variable-delay controllability results as compared to approximations that use fixed delays to model the same problems. To our knowledge, these essential capabilities are absent from existing controllability checking algorithms.\",\"bibtex\":\"@inproceedings{bhar-wil-mui-ijcai18,\\n  author    = {Nikhil Bhargava and\\n               Brian Williams and\\n               Christian Muise},\\n  title     = {Variable-Delay Controllability},\\n  booktitle = {Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ({IJCAI})},\\n  year      = {2018},\\n  abstract  = {In temporal planning, agents must schedule a set of events satisfying a set of predetermined constraints. These scheduling problems become more difficult when the duration of certain actions are outside the agent's control. Delay controllability is the generalized notion of whether a schedule can be constructed in the face of uncertainty if the agent eventually learns when events occur. Our work introduces the substantially more complex setting of determining variable-delay controllability, where an agent learns about events after some unknown but bounded amount of time has passed. We provide an efficient O(n^3) variable-delay controllability checker and show how to create an execution strategy for variable-delay controllability problems. We also provide preliminary empirical evaluations of the quality of variable-delay controllability results as compared to approximations that use fixed delays to model the same problems. To our knowledge, these essential capabilities are absent from existing controllability checking algorithms.}\\n}\\n\\n\",\"author_short\":[\"Bhargava, N.\",\"Williams, B.\",\"Muise, C.\"],\"key\":\"bhar-wil-mui-ijcai18\",\"id\":\"bhar-wil-mui-ijcai18\",\"bibbaseid\":\"bhargava-williams-muise-variabledelaycontrollability-2018\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"firstnames\":[\"Alberto\"],\"propositions\":[],\"lastnames\":[\"Camacho\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Jorge\",\"A.\"],\"propositions\":[],\"lastnames\":[\"Baier\"],\"suffixes\":[]},{\"firstnames\":[\"Sheila\",\"A.\"],\"propositions\":[],\"lastnames\":[\"McIlraith\"],\"suffixes\":[]}],\"title\":\"LTL Realizability via Safety and Reachability Games\",\"booktitle\":\"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI)\",\"year\":\"2018\",\"url_paper\":\"https://www.ijcai.org/proceedings/2018/0651.pdf\",\"url_poster\":\"https://www.cs.toronto.edu/~acamacho/papers/cam-mui-bai-mci-ijcai18-poster.pdf\",\"abstract\":\"LTL synthesis is the problem of computing a strategy that satisfies a given property expressed in Linear Temporal Logic (LTL). Synthesis provides a means of constructing programs that allow an agent to interact with their environment following a declarative specification of behavior. Modern approaches to LTL synthesis exploit bounded synthesis techniques, reducing the synthesis problem to a series of safety games between the agent and the environment. The same techniques can be used to determine unrealizability of LTL specifications by solving dual games where the order of turn taking is inverted. In the first part of the paper, we investigate the role of this duality in LTL realizability and synthesis. We describe different reductions to automata games that exploit duality to determine realizability, and introduce novel techniques that reduce realizability to a series of reachability games. In the second part of the paper, we introduce algorithms to solve these safety and reachability games via Fully Observable Non-Deterministic (FOND) planning. Our experimental evaluation illustrates that, by reducing the problem to a reachability game we can solve some problems that state-of-the-art synthesis tools cannot solve. Moreover, it shows that planning can be a competitive approach to LTL synthesis and realizability.\",\"bibtex\":\"@inproceedings{cam-mui-bai-mci-ijcai18,\\n  author    = {Alberto Camacho and\\n               Christian Muise and\\n               Jorge A. Baier and\\n               Sheila A. McIlraith},\\n  title     = {{LTL} Realizability via Safety and Reachability Games},\\n  booktitle = {Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ({IJCAI})},\\n  year      = {2018},\\n  url_paper = {https://www.ijcai.org/proceedings/2018/0651.pdf},\\n  url_poster = {https://www.cs.toronto.edu/~acamacho/papers/cam-mui-bai-mci-ijcai18-poster.pdf},\\n  abstract  = {LTL synthesis is the problem of computing a strategy that satisfies a given property expressed in Linear Temporal Logic (LTL). Synthesis provides a means of constructing programs that allow an agent to interact with their environment following a declarative specification of behavior. Modern approaches to LTL synthesis exploit bounded synthesis techniques, reducing the synthesis problem to a series of safety games between the agent and the environment. The same techniques can be used to determine unrealizability of LTL specifications by solving dual games where the order of turn taking is inverted. In the first part of the paper, we investigate the role of this duality in LTL realizability and synthesis. We describe different reductions to automata games that exploit duality to determine realizability, and introduce novel techniques that reduce realizability to a series of reachability games. In the second part of the paper, we introduce algorithms to solve these safety and reachability games via Fully Observable Non-Deterministic (FOND) planning. Our experimental evaluation illustrates that, by reducing the problem to a reachability game we can solve some problems that state-of-the-art synthesis tools cannot solve. Moreover, it shows that planning can be a competitive approach to LTL synthesis and realizability.}\\n}\\n\\n\",\"author_short\":[\"Camacho, A.\",\"Muise, C.\",\"Baier, J. A.\",\"McIlraith, S. A.\"],\"key\":\"cam-mui-bai-mci-ijcai18\",\"id\":\"cam-mui-bai-mci-ijcai18\",\"bibbaseid\":\"camacho-muise-baier-mcilraith-ltlrealizabilityviasafetyandreachabilitygames-2018\",\"role\":\"author\",\"urls\":{\" paper\":\"https://www.ijcai.org/proceedings/2018/0651.pdf\",\" poster\":\"https://www.cs.toronto.edu/~acamacho/papers/cam-mui-bai-mci-ijcai18-poster.pdf\"},\"metadata\":{\"authorlinks\":{\"camacho, a\":\"http://www.albertocamacho.com/\",\"mcilraith, s\":\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\",\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":5,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"firstnames\":[\"Alberto\"],\"propositions\":[],\"lastnames\":[\"Camacho\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Jorge\",\"A.\"],\"propositions\":[],\"lastnames\":[\"Baier\"],\"suffixes\":[]},{\"firstnames\":[\"Sheila\",\"A.\"],\"propositions\":[],\"lastnames\":[\"McIlraith\"],\"suffixes\":[]}],\"title\":\"SynKit: LTL Synthesis as a Service\",\"booktitle\":\"Demonstration Track at the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI)\",\"year\":\"2018\",\"url_paper\":\"https://www.ijcai.org/proceedings/2018/0848.pdf\",\"url_link\":\"https://www.cs.toronto.edu/~acamacho/synkit\",\"abstract\":\"Automatic synthesis of software from specification is one of the classic problems in computer science. In the last decade, significant advances have been made in the synthesis of programs from specifications expressed in Linear Temporal Logic (LTL). LTL synthesis technology is central to a myriad of applications from the automated generation of controllers for Internet of Things devices, to the synthesis of control software for robotic applications. Unfortunately, the number of existing tools for LTL synthesis is limited, and using them requires specialized expertise. In this paper we present SynKit, a tool that offers LTL synthesis as a service. SynKit integrates a RESTful API and a web service with an editor, a solver, and a strategy visualizer.\",\"bibtex\":\"@inproceedings{cam-mui-bai-mci-ijcai18demo,\\n  author    = {Alberto Camacho and\\n               Christian Muise and\\n               Jorge A. Baier and\\n               Sheila A. McIlraith},\\n  title     = {{SynKit}: {LTL} Synthesis as a Service},\\n  booktitle = {Demonstration Track at the Twenty-Seventh International Joint Conference on Artificial Intelligence ({IJCAI})},\\n  year      = {2018},\\n  url_paper = {https://www.ijcai.org/proceedings/2018/0848.pdf},\\n  url_link  = {https://www.cs.toronto.edu/~acamacho/synkit},\\n  abstract  = {Automatic synthesis of software from specification is one of the classic problems in computer science. In the last decade, significant advances have been made in the synthesis of programs from specifications expressed in Linear Temporal Logic (LTL). LTL synthesis technology is central to a myriad of applications from the automated generation of controllers for Internet of Things devices, to the synthesis of control software for robotic applications. Unfortunately, the number of existing tools for LTL synthesis is limited, and using them requires specialized expertise. In this paper we present SynKit, a tool that offers LTL synthesis as a service. SynKit integrates a RESTful API and a web service with an editor, a solver, and a strategy visualizer.}\\n}\\n\\n\",\"author_short\":[\"Camacho, A.\",\"Muise, C.\",\"Baier, J. A.\",\"McIlraith, S. A.\"],\"key\":\"cam-mui-bai-mci-ijcai18demo\",\"id\":\"cam-mui-bai-mci-ijcai18demo\",\"bibbaseid\":\"camacho-muise-baier-mcilraith-synkitltlsynthesisasaservice-2018\",\"role\":\"author\",\"urls\":{\" paper\":\"https://www.ijcai.org/proceedings/2018/0848.pdf\",\" link\":\"https://www.cs.toronto.edu/~acamacho/synkit\"},\"metadata\":{\"authorlinks\":{\"camacho, a\":\"http://www.albertocamacho.com/\",\"mcilraith, s\":\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\",\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":10,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"firstnames\":[\"Alberto\"],\"propositions\":[],\"lastnames\":[\"Camacho\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Jorge\",\"A.\"],\"propositions\":[],\"lastnames\":[\"Baier\"],\"suffixes\":[]},{\"firstnames\":[\"Sheila\",\"A.\"],\"propositions\":[],\"lastnames\":[\"McIlraith\"],\"suffixes\":[]}],\"title\":\"SynKit: Finite LTL Synthesis as a Service\",\"booktitle\":\"System Demonstrations at the Twenty-Eighth International Conference on Automated Planning and Scheduling (ICAPS)\",\"year\":\"2018\",\"url_paper\":\"http://icaps18.icaps-conference.org/fileadmin/alg/conferences/icaps18/files/demos/2_SynKit_Demo_ICAPS_18.pdf\",\"url_link\":\"https://www.cs.toronto.edu/~acamacho/synkit\",\"bibbase_note\":\"<span style=\\\"color: purple\\\">Best System Demonstration Award.</span>\",\"abstract\":\"Automatic synthesis of software from specification is one of the classical problems in computer science. Recent research has explored the use of finite linear temporal logic (LTLf) as a specification language. Engineers, researchers, and practitioners who wish to explore LTLf synthesis must overcome several barriers, including the lack of convenient tools to synthesize programs. In this paper we present SynKit, a web service that provides an LTLf synthesis capability. SynKit aims to simplify the task of synthesizing programs and debugging specifications. Offered as a web service, it is very accessible and does not require installation. SynKit integrates an editor, a solver, and a strategy visualizer.\",\"bibtex\":\"@inproceedings{cam-mui-bai-mci-icaps18demo,\\n  author    = {Alberto Camacho and\\n               Christian Muise and\\n               Jorge A. Baier and\\n               Sheila A. McIlraith},\\n  title     = {{SynKit}: Finite {LTL} Synthesis as a Service},\\n  booktitle = {System Demonstrations at the Twenty-Eighth International Conference on Automated Planning and Scheduling ({ICAPS})},\\n  year      = {2018},\\n  url_paper = {http://icaps18.icaps-conference.org/fileadmin/alg/conferences/icaps18/files/demos/2_SynKit_Demo_ICAPS_18.pdf},\\n  url_link = {https://www.cs.toronto.edu/~acamacho/synkit},\\n  bibbase_note = {<span style=\\\"color: purple\\\">Best System Demonstration Award.</span>},\\n  abstract = {Automatic synthesis of software from specification is one of the classical problems in computer science. Recent research has explored the use of finite linear temporal logic (LTLf) as a specification language. Engineers, researchers, and practitioners who wish to explore LTLf synthesis must overcome several barriers, including the lack of convenient tools to synthesize programs. In this paper we present SynKit, a web service that provides an LTLf synthesis capability. SynKit aims to simplify the task of synthesizing programs and debugging specifications. Offered as a web service, it is very accessible and does not require installation. SynKit integrates an editor, a solver, and a strategy visualizer.}\\n}\\n\\n\",\"author_short\":[\"Camacho, A.\",\"Muise, C.\",\"Baier, J. A.\",\"McIlraith, S. A.\"],\"key\":\"cam-mui-bai-mci-icaps18demo\",\"id\":\"cam-mui-bai-mci-icaps18demo\",\"bibbaseid\":\"camacho-muise-baier-mcilraith-synkitfiniteltlsynthesisasaservice-2018\",\"role\":\"author\",\"urls\":{\" paper\":\"http://icaps18.icaps-conference.org/fileadmin/alg/conferences/icaps18/files/demos/2_SynKit_Demo_ICAPS_18.pdf\",\" link\":\"https://www.cs.toronto.edu/~acamacho/synkit\"},\"metadata\":{\"authorlinks\":{\"camacho, a\":\"http://www.albertocamacho.com/\",\"mcilraith, s\":\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\",\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":14,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"firstnames\":[\"Alberto\"],\"propositions\":[],\"lastnames\":[\"Camacho\"],\"suffixes\":[]},{\"firstnames\":[\"Jorge\",\"A.\"],\"propositions\":[],\"lastnames\":[\"Baier\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Sheila\",\"A.\"],\"propositions\":[],\"lastnames\":[\"McIlraith\"],\"suffixes\":[]}],\"title\":\"Finite LTL Synthesis as Planning\",\"booktitle\":\"Proceedings of the Twenty-Eight International Conference on Automated Planning and Scheduling (ICAPS)\",\"year\":\"2018\",\"url\":\"http://www.cs.toronto.edu/~acamacho/papers/cam-bai-mui-mci-icaps18.pdf\",\"abstract\":\"LTL synthesis is the task of generating a strategy that satisfies a Linear Temporal Logic (LTL) specification interpreted over infinite traces. In this paper we examine the problem of LTLf synthesis, a variant of LTL synthesis where the specification of the behaviour of the strategy we generate is interpreted over finite traces – similar to the assumption we make in many planning problems, and important for the synthesis of business processes and other system interactions of finite duration. Existing approaches to LTLf synthesis transform LTLf into deterministic finite-state automata (DFA) and reduce the synthesis problem to a DFA game. Unfortunately, the DFA transformation is worst-case double-exponential in the size of the formula, presenting a computational bottleneck. In contrast, our approach exploits non-deterministic automata, and we reduce the synthesis problem to a non-deterministic planning problem. We leverage our approach not only for strategy generation but also to generate certificates of unrealizability – the first such method for LTLf. We employ a battery of techniques that exploit the structure of the LTLf specification to improve the efficiency of our transformation to automata. We combine these techniques with lazy determinization of automata and on-the-fly state abstraction. We illustrate the effectiveness of our approach on a set of established LTL synthesis benchmarks adapted to finite LTL.\",\"bibtex\":\"@inproceedings{cam-bai-mui-mci-icaps18,\\n  author    = {Alberto Camacho and\\n               Jorge A. Baier and\\n               Christian Muise and\\n               Sheila A. McIlraith},\\n  title     = {Finite {LTL} Synthesis as Planning},\\n  booktitle = {Proceedings of the Twenty-Eight International Conference on Automated Planning and Scheduling ({ICAPS})},\\n  year      = {2018},\\n  url       = {http://www.cs.toronto.edu/~acamacho/papers/cam-bai-mui-mci-icaps18.pdf},\\n  abstract  = {LTL synthesis is the task of generating a strategy that satisfies a Linear Temporal Logic (LTL) specification interpreted over infinite traces. In this paper we examine the problem of LTLf synthesis, a variant of LTL synthesis where the specification of the behaviour of the strategy we generate is interpreted over finite traces – similar to the assumption we make in many planning problems, and important for the synthesis of business processes and other system interactions of finite duration. Existing approaches to LTLf synthesis transform LTLf into deterministic finite-state automata (DFA) and reduce the synthesis problem to a DFA game. Unfortunately, the DFA transformation is worst-case double-exponential in the size of the formula, presenting a computational bottleneck. In contrast, our approach exploits non-deterministic automata, and we reduce the synthesis problem to a non-deterministic planning problem. We leverage our approach not only for strategy generation but also to generate certificates of unrealizability – the first such method for LTLf. We employ a battery of techniques that exploit the structure of the LTLf specification to improve the efficiency of our transformation to automata. We combine these techniques with lazy determinization of automata and on-the-fly state abstraction. We illustrate the effectiveness of our approach on a set of established LTL synthesis benchmarks adapted to finite LTL.}\\n}\\n\\n\",\"author_short\":[\"Camacho, A.\",\"Baier, J. A.\",\"Muise, C.\",\"McIlraith, S. A.\"],\"key\":\"cam-bai-mui-mci-icaps18\",\"id\":\"cam-bai-mui-mci-icaps18\",\"bibbaseid\":\"camacho-baier-muise-mcilraith-finiteltlsynthesisasplanning-2018\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.cs.toronto.edu/~acamacho/papers/cam-bai-mui-mci-icaps18.pdf\"},\"metadata\":{\"authorlinks\":{\"camacho, a\":\"http://www.albertocamacho.com/\",\"mcilraith, s\":\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\",\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":27,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"firstnames\":[\"Alberto\"],\"propositions\":[],\"lastnames\":[\"Camacho\"],\"suffixes\":[]},{\"firstnames\":[\"Jorge\",\"A.\"],\"propositions\":[],\"lastnames\":[\"Baier\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Sheila\",\"A.\"],\"propositions\":[],\"lastnames\":[\"McIlraith\"],\"suffixes\":[]}],\"title\":\"Synthesizing Controllers: On the Correspondence Between LTL Synthesis and Non-deterministic Planning\",\"booktitle\":\"Advances in Artificial Intelligence - Proceedings of the Thirty-First Canadian Conference on Artificial Intelligence (CCAI)\",\"pages\":\"45–59\",\"year\":\"2018\",\"url\":\"https://doi.org/10.1007/978-3-319-89656-4_4\",\"url_paper\":\"http://www.cs.toronto.edu/~acamacho/papers/cam-bai-mui-mci-cai2018.pdf\",\"abstract\":\"Linear Temporal Logic (LTL) synthesis can be understood as the problem of building a controller that defines a winning strategy, for a two-player game against the environment, where the objective is to satisfy a given LTL formula. It is an important problem with applications in software synthesis, including controller synthesis. In this paper we establish the correspondence between LTL synthesis and fully observable non-deterministic (FOND) planning. We study LTL interpreted over both finite and infinite traces. We also provide the first explicit compilation that translates an LTL synthesis problem to a FOND problem. Experiments with state-of-the-art LTL FOND and synthesis solvers show automated planning to be a viable and effective tool for highly structured LTL synthesis problems.\",\"bibtex\":\"@inproceedings{cam-bai-mui-mci-ccai18,\\n  author    = {Alberto Camacho and\\n               Jorge A. Baier and\\n               Christian Muise and\\n               Sheila A. McIlraith},\\n  title     = {Synthesizing Controllers: On the Correspondence Between {LTL} Synthesis and Non-deterministic Planning},\\n  booktitle = {Advances in Artificial Intelligence - Proceedings of the Thirty-First Canadian Conference on Artificial Intelligence ({CCAI})},\\n  pages     = {45--59},\\n  year      = {2018},\\n  url       = {https://doi.org/10.1007/978-3-319-89656-4_4},\\n  url_paper = {http://www.cs.toronto.edu/~acamacho/papers/cam-bai-mui-mci-cai2018.pdf},\\n  abstract  = {Linear Temporal Logic (LTL) synthesis can be understood as the problem of building a controller that defines a winning strategy, for a two-player game against the environment, where the objective is to satisfy a given LTL formula. It is an important problem with applications in software synthesis, including controller synthesis. In this paper we establish the correspondence between LTL synthesis and fully observable non-deterministic (FOND) planning. We study LTL interpreted over both finite and infinite traces. We also provide the first explicit compilation that translates an LTL synthesis problem to a FOND problem. Experiments with state-of-the-art LTL FOND and synthesis solvers show automated planning to be a viable and effective tool for highly structured LTL synthesis problems.}\\n}\\n\\n\",\"author_short\":[\"Camacho, A.\",\"Baier, J. A.\",\"Muise, C.\",\"McIlraith, S. A.\"],\"key\":\"cam-bai-mui-mci-ccai18\",\"id\":\"cam-bai-mui-mci-ccai18\",\"bibbaseid\":\"camacho-baier-muise-mcilraith-synthesizingcontrollersonthecorrespondencebetweenltlsynthesisandnondeterministicplanning-2018\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://doi.org/10.1007/978-3-319-89656-4_4\",\" paper\":\"http://www.cs.toronto.edu/~acamacho/papers/cam-bai-mui-mci-cai2018.pdf\"},\"metadata\":{\"authorlinks\":{\"camacho, a\":\"http://www.albertocamacho.com/\",\"mcilraith, s\":\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\",\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":5,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"New Orleans, LA\",\"author\":[{\"firstnames\":[\"Jingkai\"],\"propositions\":[],\"lastnames\":[\"Chen\"],\"suffixes\":[]},{\"firstnames\":[\"Cheng\"],\"propositions\":[],\"lastnames\":[\"Fang\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Peng\"],\"propositions\":[],\"lastnames\":[\"Yu\"],\"suffixes\":[]},{\"firstnames\":[\"Howard\",\"E.\"],\"propositions\":[],\"lastnames\":[\"Shrobe\"],\"suffixes\":[]},{\"firstnames\":[\"Brian\",\"C.\"],\"propositions\":[],\"lastnames\":[\"Williams\"],\"suffixes\":[]}],\"booktitle\":\"AAAI Workshop on Artificial Intelligence for Cyber Security (AICS-2018)\",\"title\":\"RADMAX: Risk and Deadline Aware Planning for Maximum Utility\",\"year\":\"2018\",\"url\":\"http://groups.csail.mit.edu/mers/publication_uploads/Publications/2018/AAAI18Chen/radmaxAAAI18.pdf\",\"abstract\":\"Current network approaches aim to maximize network utilization when routing flows. While such approaches are fast and usually result in acceptable behavior, existing methods are not mission aware. There is no concept of utility maximization, no capability to handle flows with specified deadlines and loss requirements, and no guarantees over the probability of network saturation. In this paper, we present RADMAX: a system for Risk And Deadline Aware Planning for Maximum Utility based on constraint programming, which allows us to handle higher level mission specifications. We show the correctness of RADMAX with respect to loss and delay bounds, provide results for the optimality of RADMAX with respect to the mission utility, and review current results on computational performance\",\"bibtex\":\"@inproceedings{Chen2018,\\n address = {New Orleans, LA},\\n author = {Jingkai Chen and Cheng Fang and Christian Muise and Peng Yu and Howard E. Shrobe and Brian C. Williams},\\n booktitle = {AAAI Workshop on Artificial Intelligence for Cyber Security (AICS-2018)},\\n title = {RADMAX: Risk and Deadline Aware Planning for Maximum Utility},\\n year = {2018},\\n url = {http://groups.csail.mit.edu/mers/publication_uploads/Publications/2018/AAAI18Chen/radmaxAAAI18.pdf},\\n abstract = {Current  network  approaches  aim  to  maximize  network  utilization when routing flows. While such approaches are fast\\nand usually result in acceptable behavior, existing methods are not\\nmission aware. There is no concept of utility maximization, no capability to handle flows with specified deadlines and loss requirements, and no guarantees over the probability of network saturation.\\nIn this paper, we present RADMAX: a system for Risk And\\nDeadline Aware Planning for Maximum Utility based on constraint programming, which allows us to handle higher level\\nmission  specifications.  We  show  the  correctness  of  RADMAX with respect to loss and delay bounds, provide results\\nfor the optimality of RADMAX with respect to the mission\\nutility,  and  review  current  results  on  computational  performance}\\n}\\n\\n\",\"author_short\":[\"Chen, J.\",\"Fang, C.\",\"Muise, C.\",\"Yu, P.\",\"Shrobe, H. E.\",\"Williams, B. C.\"],\"key\":\"Chen2018\",\"id\":\"Chen2018\",\"bibbaseid\":\"chen-fang-muise-yu-shrobe-williams-radmaxriskanddeadlineawareplanningformaximumutility-2018\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://groups.csail.mit.edu/mers/publication_uploads/Publications/2018/AAAI18Chen/radmaxAAAI18.pdf\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Bridging the Gap Between LTL Synthesis and Automated Planning\",\"author\":[{\"firstnames\":[\"Alberto\"],\"propositions\":[],\"lastnames\":[\"Camacho\"],\"suffixes\":[]},{\"firstnames\":[\"Jorge\"],\"propositions\":[],\"lastnames\":[\"Baier\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Sheila\",\"A.\"],\"propositions\":[],\"lastnames\":[\"McIlraith\"],\"suffixes\":[]}],\"booktitle\":\"Workshop on Generalized Planning (GenPlan'17)\",\"year\":\"2017\",\"url\":\"http://www.cs.toronto.edu/~sheila/publications/cam-etal-genplan17.pdf\",\"abstract\":\"Linear Temporal Logic (LTL) synthesis can be understood as the problem of building a controller that defines a winning strategy, for a two-player game against the environment, where the objective is to satisfy a given LTL formula. It is an important problem with applications in software synthesis, including controller synthesis. Recent work has explored the close connection between automated planning and LTL synthesis but has not provided a full mapping between the two problems nor have its practical implications been explored. In this paper we establish the correspondence between LTL synthesis and fully observable non-deterministic (FOND) planning. We also provide the first explicit compilation that translates an LTL synthesis problem to a FOND problem. Experiments with state-of-the-art LTL FOND and synthesis solvers show automated planning to be a viable and effective tool for highly structured LTL synthesis problems.\",\"bibtex\":\"@inproceedings{camacho-genplan-17,\\n  title = {Bridging the Gap Between LTL Synthesis and Automated Planning},\\n  author = {Alberto Camacho and Jorge Baier and Christian Muise and Sheila A. McIlraith},\\n  booktitle = {Workshop on Generalized Planning ({GenPlan}'17)},\\n  year = {2017},\\n  url = {http://www.cs.toronto.edu/~sheila/publications/cam-etal-genplan17.pdf},\\n  abstract={Linear Temporal Logic (LTL) synthesis can be understood as the problem of building a controller that defines a winning strategy, for a two-player game against the environment, where the objective is to satisfy a given LTL formula. It is an important problem with applications in software synthesis, including controller synthesis. Recent work has explored the close connection between automated planning and LTL synthesis but has not provided a full mapping between the two problems nor have its practical implications been explored. In this paper we establish the correspondence between LTL synthesis and fully observable non-deterministic (FOND) planning. We also provide the first explicit compilation that translates an LTL synthesis problem to a FOND problem. Experiments with state-of-the-art LTL FOND and synthesis solvers show automated planning to be a viable and effective tool for highly structured LTL synthesis problems.}\\n}\\n\\n\\n\",\"author_short\":[\"Camacho, A.\",\"Baier, J.\",\"Muise, C.\",\"McIlraith, S. A.\"],\"key\":\"camacho-genplan-17\",\"id\":\"camacho-genplan-17\",\"bibbaseid\":\"camacho-baier-muise-mcilraith-bridgingthegapbetweenltlsynthesisandautomatedplanning-2017\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.cs.toronto.edu/~sheila/publications/cam-etal-genplan17.pdf\"},\"metadata\":{\"authorlinks\":{\"camacho, a\":\"http://www.albertocamacho.com/\",\"mcilraith, s\":\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\",\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":8,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"firstnames\":[\"Brent\"],\"propositions\":[],\"lastnames\":[\"Mombourquette\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Sheila\"],\"propositions\":[],\"lastnames\":[\"McIlraith\"],\"suffixes\":[]}],\"year\":\"2017\",\"booktitle\":\"The 31st AAAI Conference on Artificial Intelligence\",\"keywords\":\"state estimation, belief tracking, logical filtering, approximation\",\"title\":\"Logical Filtering and Smoothing: State Estimation in Partially Observable Domains\",\"url\":\"http://www.haz.ca/papers/momb-aaai17.pdf\",\"abstract\":\"State estimation is the task of estimating the state of a partially observable dynamical system given a sequence of executed actions and observations. In logical settings, state estimation can be realized via logical filtering, which is exact but can be intractable. We propose logical smoothing, a form of backwards reasoning that works in concert with approximated logical filtering to refine past beliefs in light of new observations. We characterize the notion of logical smoothing together with an algorithm for backwards-forwards state estimation. We also present an approximation of our smoothing algorithm that is space efficient. We prove properties of our algorithms, and experimentally demonstrate their behaviour, contrasting them with state estimation methods for planning. Smoothing and backwards-forwards reasoning are important techniques for reasoning about partially observable dynamical systems, introducing the logical analogue of effective techniques from control theory and dynamic programming.\",\"bibtex\":\"@inproceedings{bfr-aaai-17,\\n  author = {Brent Mombourquette and Christian Muise and Sheila McIlraith},\\n  year = {2017},\\n  booktitle = {The 31st AAAI Conference on Artificial Intelligence},\\n  keywords = {state estimation, belief tracking, logical filtering, approximation},\\n  title = {Logical Filtering and Smoothing: State Estimation in Partially Observable Domains},\\n  url = {http://www.haz.ca/papers/momb-aaai17.pdf},\\n  abstract = {State estimation is the task of estimating the state of a partially\\nobservable dynamical system given a sequence of executed\\nactions and observations. In logical settings, state estimation\\ncan be realized via logical filtering, which is exact\\nbut can be intractable. We propose logical smoothing, a form\\nof backwards reasoning that works in concert with approximated\\nlogical filtering to refine past beliefs in light of new observations.\\nWe characterize the notion of logical smoothing\\ntogether with an algorithm for backwards-forwards state estimation.\\nWe also present an approximation of our smoothing\\nalgorithm that is space efficient. We prove properties of our\\nalgorithms, and experimentally demonstrate their behaviour,\\ncontrasting them with state estimation methods for planning.\\nSmoothing and backwards-forwards reasoning are important\\ntechniques for reasoning about partially observable dynamical\\nsystems, introducing the logical analogue of effective\\ntechniques from control theory and dynamic programming.}\\n}\\n\\n\",\"author_short\":[\"Mombourquette, B.\",\"Muise, C.\",\"McIlraith, S.\"],\"key\":\"bfr-aaai-17\",\"id\":\"bfr-aaai-17\",\"bibbaseid\":\"mombourquette-muise-mcilraith-logicalfilteringandsmoothingstateestimationinpartiallyobservabledomains-2017\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.haz.ca/papers/momb-aaai17.pdf\"},\"keyword\":[\"state estimation\",\"belief tracking\",\"logical filtering\",\"approximation\"],\"metadata\":{\"authorlinks\":{\"mcilraith, s\":\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\",\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"firstnames\":[\"Alberto\"],\"propositions\":[],\"lastnames\":[\"Camacho\"],\"suffixes\":[]},{\"firstnames\":[\"Eleni\"],\"propositions\":[],\"lastnames\":[\"Triantafillou\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Jorge\"],\"propositions\":[],\"lastnames\":[\"Baier\"],\"suffixes\":[]},{\"firstnames\":[\"Sheila\",\"A.\"],\"propositions\":[],\"lastnames\":[\"McIlraith\"],\"suffixes\":[]}],\"year\":\"2017\",\"booktitle\":\"The 31st AAAI Conference on Artificial Intelligence\",\"keywords\":\"LTL, non-deterministic planning, temporally extended goals, FOND\",\"title\":\"Non-Deterministic Planning with Temporally Extended Goals: LTL over finite and infinite traces\",\"url\":\"http://www.haz.ca/papers/camacho-aaai17.pdf\",\"abstract\":\"Temporally extended goals are critical to the specification of a diversity of real-world planning problems. Here we examine the problem of non-deterministic planning with temporally extended goals specified in linear temporal logic (LTL), interpreted over either finite or infinite traces. Unlike existing LTL planners, we place no restrictions on our LTL formulae beyond those necessary to distinguish finite from infinite interpretations. We generate plans by compiling LTL temporally extended goals into problem instances described in the Planning Domain Definition Language that are solved by a state-of-the-art fully observable non-deterministic planner. We propose several different compilations based on translations of LTL to alternating or non-deterministic (B¨uchi) automata, and evaluate various properties of the competing approaches. We address a diverse spectrum of LTL planning problems that, to this point, had not been solvable using AI planning techniques, and do so in a manner that demonstrates highly competitive performance.\",\"bibtex\":\"@inproceedings{ltl-fond-aaai-17,\\n  author = {Alberto Camacho and Eleni Triantafillou and Christian Muise and Jorge Baier and Sheila A. McIlraith},\\n  year = {2017},\\n  booktitle = {The 31st AAAI Conference on Artificial Intelligence},\\n  keywords = {LTL, non-deterministic planning, temporally extended goals, FOND},\\n  title = {Non-Deterministic Planning with Temporally Extended Goals: {LTL} over finite and infinite traces},\\n  url = {http://www.haz.ca/papers/camacho-aaai17.pdf},\\n  abstract = {Temporally extended goals are critical to the specification of a\\ndiversity of real-world planning problems. Here we examine\\nthe problem of non-deterministic planning with temporally\\nextended goals specified in linear temporal logic (LTL), interpreted\\nover either finite or infinite traces. Unlike existing\\nLTL planners, we place no restrictions on our LTL formulae\\nbeyond those necessary to distinguish finite from infinite\\ninterpretations. We generate plans by compiling LTL temporally\\nextended goals into problem instances described in\\nthe Planning Domain Definition Language that are solved by\\na state-of-the-art fully observable non-deterministic planner.\\nWe propose several different compilations based on translations\\nof LTL to alternating or non-deterministic (B¨uchi) automata,\\nand evaluate various properties of the competing approaches.\\nWe address a diverse spectrum of LTL planning\\nproblems that, to this point, had not been solvable using AI\\nplanning techniques, and do so in a manner that demonstrates\\nhighly competitive performance.}\\n}\\n\\n\\n\",\"author_short\":[\"Camacho, A.\",\"Triantafillou, E.\",\"Muise, C.\",\"Baier, J.\",\"McIlraith, S. A.\"],\"key\":\"ltl-fond-aaai-17\",\"id\":\"ltl-fond-aaai-17\",\"bibbaseid\":\"camacho-triantafillou-muise-baier-mcilraith-nondeterministicplanningwithtemporallyextendedgoalsltloverfiniteandinfinitetraces-2017\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.haz.ca/papers/camacho-aaai17.pdf\"},\"keyword\":[\"LTL\",\"non-deterministic planning\",\"temporally extended goals\",\"FOND\"],\"metadata\":{\"authorlinks\":{\"camacho, a\":\"http://www.albertocamacho.com/\",\"mcilraith, s\":\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\",\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":31,\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"author\":[{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]}],\"title\":\"A Flow Shop Scheduler\",\"editor\":[{\"propositions\":[],\"lastnames\":[\"Brown\"],\"firstnames\":[\"Amy\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Dibernardo\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]}],\"booktitle\":\"500 Lines or Less\",\"publisher\":\"Lulu.com\",\"year\":\"2016\",\"url\":\"http://aosabook.org/en/500L/a-flow-shop-scheduler.html\",\"chapter\":\"9\",\"bibtex\":\"@incollection{brown2016500,\\n  author      = {Christian Muise},\\n  title       = {A Flow Shop Scheduler},\\n  editor      = {Brown, Amy and Dibernardo, Michael},\\n  booktitle   = {500 Lines or Less},\\n  publisher   = {Lulu.com},\\n  year        = 2016,\\n  %pages       = {},\\n  url         = {http://aosabook.org/en/500L/a-flow-shop-scheduler.html},\\n  chapter     = 9,\\n}\\n\\n\",\"author_short\":[\"Muise, C.\"],\"editor_short\":[\"Brown, A.\",\"Dibernardo, M.\"],\"key\":\"brown2016500\",\"id\":\"brown2016500\",\"bibbaseid\":\"muise-aflowshopscheduler-2016\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://aosabook.org/en/500L/a-flow-shop-scheduler.html\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Towards Team Formation via Automated Planning\",\"author\":[{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Frank\"],\"propositions\":[],\"lastnames\":[\"Dignum\"],\"suffixes\":[]},{\"firstnames\":[\"Paolo\"],\"propositions\":[],\"lastnames\":[\"Felli\"],\"suffixes\":[]},{\"firstnames\":[\"Tim\"],\"propositions\":[],\"lastnames\":[\"Miller\"],\"suffixes\":[]},{\"firstnames\":[\"Adrian\",\"R.\"],\"propositions\":[],\"lastnames\":[\"Pearce\"],\"suffixes\":[]},{\"firstnames\":[\"Liz\"],\"propositions\":[],\"lastnames\":[\"Sonenberg\"],\"suffixes\":[]}],\"journal\":\"Lecture Notes in Computer Science\",\"volume\":\"9628\",\"publisher\":\"Springer\",\"pages\":\"282-299\",\"year\":\"2016\",\"note\":\"Special Issue on Coordination, Organizations, Institutions, and Norms in Agent Systems XI\",\"keywords\":\"multi-agent planning, epistemic reasoning\",\"url\":\"http://link.springer.com/chapter/10.1007%2F978-3-319-42691-4_16\",\"abstract\":\"Cooperative problem solving involves four key phases: (1) finding potential members to form a team, (2) forming the team, (3) formulating a plan for the team, and (4) executing the plan. We extend recent work on multi-agent epistemic planning and apply it to the problem of team formation in a blocksworld scenario. We provide an encoding of the first three phases of team formation from the perspective of an initiator, and show how automated planning efficiently yields conditional plans that guarantee certain collective intentions will be achieved. The expressiveness of the epistemic planning formalism, which supports modelling with the nested beliefs of agents, opens the prospect of broad applicability to the operationalisation of collective intention.\",\"bibtex\":\"@article{coin-journal-teamwork,\\n  title = {Towards Team Formation via Automated Planning},\\n  author = {Christian Muise and Frank Dignum and Paolo Felli and Tim Miller and Adrian R. Pearce and Liz Sonenberg},\\n  journal = {Lecture Notes in Computer Science},\\n  volume = {9628},\\n  publisher = {Springer},\\n  pages = {282-299},\\n  year = {2016},\\n  note = {Special Issue on Coordination, Organizations, Institutions, and Norms in Agent Systems XI},\\n  keywords = {multi-agent planning, epistemic reasoning},\\n  url = {http://link.springer.com/chapter/10.1007%2F978-3-319-42691-4_16},\\n  abstract={Cooperative problem solving involves four key phases: (1) finding potential members to form a team, (2) forming the team, (3) formulating a plan for the team, and (4) executing the plan. We extend recent work on multi-agent epistemic planning and apply it to the problem of team formation in a blocksworld scenario. We provide an encoding of the first three phases of team formation from the perspective of an initiator, and show how automated planning efficiently yields conditional plans that guarantee certain collective intentions will be achieved. The expressiveness of the epistemic planning formalism, which supports modelling with the nested beliefs of agents, opens the prospect of broad applicability to the operationalisation of collective intention.}\\n}\\n\\n\",\"author_short\":[\"Muise, C.\",\"Dignum, F.\",\"Felli, P.\",\"Miller, T.\",\"Pearce, A. R.\",\"Sonenberg, L.\"],\"key\":\"coin-journal-teamwork\",\"id\":\"coin-journal-teamwork\",\"bibbaseid\":\"muise-dignum-felli-miller-pearce-sonenberg-towardsteamformationviaautomatedplanning-2016\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://link.springer.com/chapter/10.1007%2F978-3-319-42691-4_16\"},\"keyword\":[\"multi-agent planning\",\"epistemic reasoning\"],\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":3,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Belief State Estimation for Planning via Approximate Logical Filtering and Smoothing\",\"author\":[{\"firstnames\":[\"Brent\"],\"propositions\":[],\"lastnames\":[\"Mombourquette\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Sheila\",\"A.\"],\"propositions\":[],\"lastnames\":[\"McIlraith\"],\"suffixes\":[]}],\"booktitle\":\"Workshop on Knowledge-based techniques for problem solving and reasoning (KnowProS'16)\",\"year\":\"2016\",\"url\":\"http://ceur-ws.org/Vol-1648/paper8.pdf\",\"abstract\":\"State estimation is the task of estimating the state of a partially observable dynamical system given a sequence of executed actions and observations. In logical settings, state estimation can be realized via logical filtering. Unfortunately such filtering, though exact, can be intractable. To this end, we propose logical smoothing, a form of backwards reasoning that works in concert with logical filtering to refine past beliefs in light of new observations. We characterize the notion of logical smoothing together with an algorithm for backwards-forwards state estimation. We prove properties of our algorithms, and experimentally demonstrate their behaviour. Smoothing together with backwards-forwards reasoning are important techniques for reasoning about partially observable dynamical systems, introducing the logical analogue of effective techniques from control theory and dynamic programming\",\"bibtex\":\"@inproceedings{mombourquette-knowpros16-bf,\\n  title = {Belief State Estimation for Planning via Approximate Logical Filtering and Smoothing},\\n  author = {Brent Mombourquette and Christian Muise and Sheila A. McIlraith},\\n  booktitle = {Workshop on Knowledge-based techniques for problem solving and reasoning ({KnowProS}'16)},\\n  year = {2016},\\n  url = {http://ceur-ws.org/Vol-1648/paper8.pdf},\\n  abstract={State estimation is the task of estimating the state of a partially observable dynamical system given a sequence of executed actions and observations. In logical settings, state estimation can be realized via logical filtering. Unfortunately such filtering, though exact, can be intractable. To this end, we propose logical smoothing, a form of backwards reasoning that works in concert with logical filtering to refine past beliefs in light of new observations. We characterize the notion of logical smoothing together with an algorithm for backwards-forwards state estimation. We prove properties of our algorithms, and experimentally demonstrate their behaviour. Smoothing together with backwards-forwards reasoning are important techniques for reasoning about partially observable dynamical systems, introducing the logical analogue of effective techniques from control theory and dynamic programming}\\n}\\n\\n\",\"author_short\":[\"Mombourquette, B.\",\"Muise, C.\",\"McIlraith, S. A.\"],\"key\":\"mombourquette-knowpros16-bf\",\"id\":\"mombourquette-knowpros16-bf\",\"bibbaseid\":\"mombourquette-muise-mcilraith-beliefstateestimationforplanningviaapproximatelogicalfilteringandsmoothing-2016\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://ceur-ws.org/Vol-1648/paper8.pdf\"},\"metadata\":{\"authorlinks\":{\"mcilraith, s\":\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\",\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Non-Deterministic Planning with Temporally Extended Goals: Completing the story for finite and infinite LTL\",\"author\":[{\"firstnames\":[\"Alberto\"],\"propositions\":[],\"lastnames\":[\"Camacho\"],\"suffixes\":[]},{\"firstnames\":[\"Eleni\"],\"propositions\":[],\"lastnames\":[\"Triantafillou\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Jorge\"],\"propositions\":[],\"lastnames\":[\"Baier\"],\"suffixes\":[]},{\"firstnames\":[\"Sheila\",\"A.\"],\"propositions\":[],\"lastnames\":[\"McIlraith\"],\"suffixes\":[]}],\"booktitle\":\"Workshop on Knowledge-based techniques for problem solving and reasoning (KnowProS'16)\",\"year\":\"2016\",\"url\":\"http://ceur-ws.org/Vol-1648/paper10.pdf\",\"abstract\":\"Temporally extended goals are critical to the specification of a diversity of real-world planning problems. Here we examine the problem of planning with temporally extended goals over both finite and infinite traces where actions can be non-deterministic, and where temporally extended goals are specified in linear temporal logic (LTL). Unlike existing LTL planners, we place no restrictionson our LTL formulae beyond those necessary to distinguish finite from infinite trace interpretations. We realize our planner by compiling temporally extended goals, represented in LTL, into Planning Domain Definition Language problem instances, and exploiting a state-of-the-art fully observable non-deterministic planner to compute solutions. The resulting planner is sound and complete. Our approach exploits the correspondence between LTL and automata. We propose several different compilations based on translations of LTL to (Buchi) alternating or non-deterministic finite state automata, and evaluate various properties of the competing approaches. We address a diverse spectrum of LTL planning problems that, to this point, had not been solvable using AI planning techniques. We do so while demonstrating competitive performance relative to the state of the art in LTL planning.\",\"bibtex\":\"@inproceedings{camacho-knowpros16-ltl,\\n  title = {Non-Deterministic Planning with Temporally Extended Goals: Completing the story for finite and infinite LTL},\\n  author = {Alberto Camacho and Eleni Triantafillou and Christian Muise and Jorge Baier and Sheila A. McIlraith},\\n  booktitle = {Workshop on Knowledge-based techniques for problem solving and reasoning ({KnowProS}'16)},\\n  year = {2016},\\n  url = {http://ceur-ws.org/Vol-1648/paper10.pdf},\\n  abstract={Temporally extended goals are critical to the specification of a diversity of real-world planning problems. Here we examine the problem of planning with temporally extended goals over both finite and infinite traces where actions can be non-deterministic, and where temporally extended goals are specified in linear temporal logic (LTL). Unlike existing LTL planners, we place no restrictionson our LTL formulae beyond those necessary to distinguish finite from infinite trace interpretations. We realize our planner by compiling temporally extended goals, represented in LTL, into Planning Domain Definition Language problem instances, and exploiting a state-of-the-art fully observable non-deterministic planner to compute solutions. The resulting planner is sound and complete. Our approach exploits the correspondence between LTL and automata. We propose several different compilations based on translations of LTL to (Buchi) alternating or non-deterministic finite state automata, and evaluate various properties of the competing approaches. We address a diverse spectrum of LTL planning problems that, to this point, had not been solvable using AI planning techniques. We do so while demonstrating competitive performance relative to the state of the art in LTL planning.}\\n}\\n\\n\",\"author_short\":[\"Camacho, A.\",\"Triantafillou, E.\",\"Muise, C.\",\"Baier, J.\",\"McIlraith, S. A.\"],\"key\":\"camacho-knowpros16-ltl\",\"id\":\"camacho-knowpros16-ltl\",\"bibbaseid\":\"camacho-triantafillou-muise-baier-mcilraith-nondeterministicplanningwithtemporallyextendedgoalscompletingthestoryforfiniteandinfiniteltl-2016\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://ceur-ws.org/Vol-1648/paper10.pdf\"},\"metadata\":{\"authorlinks\":{\"camacho, a\":\"http://www.albertocamacho.com/\",\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"author\":[{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"J.\",\"Christopher\"],\"propositions\":[],\"lastnames\":[\"Beck\"],\"suffixes\":[]},{\"firstnames\":[\"Sheila\",\"A.\"],\"propositions\":[],\"lastnames\":[\"McIlraith\"],\"suffixes\":[]}],\"title\":\"Optimal Partial-Order Plan Relaxation via MaxSAT\",\"journal\":\"Journal of Artificial Intelligence Research\",\"year\":\"2016\",\"url\":\"http://www.jair.org/media/5128/live-5128-9534-jair.pdf\",\"abstract\":\"Partial-order plans (POPs) are attractive because of their least-commitment nature, which provides enhanced plan flexibility at execution time relative to sequential plans. Current research on automated plan generation focuses on producing sequential plans, despite the appeal of POPs. In this paper we examine POP generation by relaxing or modifying the action orderings of a sequential plan to optimize for plan criteria that promote flexibility. Our approach relies on a novel partial weighted MaxSAT encoding of a sequential plan that supports the minimization of deordering or reordering of actions. We further demonstrate how to remove redundant actions from the plan. Our partial weighted MaxSAT encoding allows us to compute a POP from a sequential plan effectively. We compare the efficiency of our approach to previous methods for POP generation via sequential-plan relaxation. Our results show that while an existing heuristic approach consistently produces the optimal deordering of a sequential plan, our approach has greater flexibility when we consider reordering the actions in the plan while also providing a guarantee of optimality. We also investigate and confirm the accuracy of the standard $\\\\flex$ metric typically used to predict the true flexibility of a POP as measured by the number of linearizations it represents.\",\"bibtex\":\"@article{jair-popgen,\\n  author    = {Christian Muise and J. Christopher Beck and Sheila A. McIlraith},\\n  title     = {Optimal Partial-Order Plan Relaxation via MaxSAT},\\n  journal   = {Journal of Artificial Intelligence Research},\\n  year      = {2016},\\n  url       = {http://www.jair.org/media/5128/live-5128-9534-jair.pdf},\\n  abstract  = {Partial-order plans (POPs) are attractive because of their least-commitment nature, which provides enhanced plan flexibility at execution time relative to sequential plans. Current research on automated plan generation focuses on producing sequential plans, despite the appeal of POPs. In this paper we examine POP generation by relaxing or modifying the action orderings of a sequential plan to optimize for plan criteria that promote flexibility. Our approach relies on a novel partial weighted MaxSAT encoding of a sequential plan that supports the minimization of deordering or reordering of actions. We further demonstrate how to remove redundant actions from the plan. Our partial weighted MaxSAT encoding allows us to compute a POP from a sequential plan effectively. We compare the efficiency of our approach to previous methods for POP generation via sequential-plan relaxation. Our results show that while an existing heuristic approach consistently produces the optimal deordering of a sequential plan, our approach has greater flexibility when we consider reordering the actions in the plan while also providing a guarantee of optimality. We also investigate and confirm the accuracy of the standard $\\\\flex$ metric typically used to predict the true flexibility of a POP as measured by the number of linearizations it represents.}\\n}\\n\\n\",\"author_short\":[\"Muise, C.\",\"Beck, J. C.\",\"McIlraith, S. A.\"],\"key\":\"jair-popgen\",\"id\":\"jair-popgen\",\"bibbaseid\":\"muise-beck-mcilraith-optimalpartialorderplanrelaxationviamaxsat-2016\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.jair.org/media/5128/live-5128-9534-jair.pdf\"},\"metadata\":{\"authorlinks\":{\"mcilraith, s\":\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\",\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":3,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Planning.Domains\",\"author\":[{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]}],\"booktitle\":\"The 26th International Conference on Automated Planning and Scheduling - Demonstrations\",\"year\":\"2016\",\"url\":\"http://www.haz.ca/papers/planning-domains-icaps16.pdf\",\"abstract\":\"Commonly used resources for the field of automated planning, such as benchmarks, problem generators, etc., are widespread over the internet. With planning.domains, we aim to (a) collect these resources in a central location; and (b) enable creative possibilities through a consistent interface to the larger planning community. In this demo, we focus on the three main pillars of planning.domains: (1) api.planning.domains – a programmatic interface to all existing planning problems; (2) solver.planning.domains – an open (and extendable) interface to planning-in-the-cloud; and (3) editor.planning.domains - a fully featured editor for planning domains.\",\"bibtex\":\"@inproceedings{muise-icaps16demo-pd,\\n  title = {{Planning.Domains}},\\n  author = {Christian Muise},\\n  booktitle = {The 26th International Conference on Automated Planning and Scheduling - Demonstrations},\\n  year = {2016},\\n  url = {http://www.haz.ca/papers/planning-domains-icaps16.pdf},\\n  abstract = {Commonly used resources for the field of automated planning, such as benchmarks, problem generators, etc., are widespread over the internet. With planning.domains, we aim to (a) collect these resources in a central location; and (b) enable creative possibilities through a consistent interface to the larger planning community. In this demo, we focus on the three main pillars of planning.domains: (1) api.planning.domains – a programmatic interface to all existing planning problems; (2) solver.planning.domains – an open (and extendable) interface to planning-in-the-cloud; and (3) editor.planning.domains - a fully featured editor for planning domains.}\\n}\\n\\n\",\"author_short\":[\"Muise, C.\"],\"key\":\"muise-icaps16demo-pd\",\"id\":\"muise-icaps16demo-pd\",\"bibbaseid\":\"muise-planningdomains-2016\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.haz.ca/papers/planning-domains-icaps16.pdf\"},\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":7,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"LTL Synthesis for Non-Deterministic Systems on Finite and Infinite Traces\",\"author\":[{\"firstnames\":[\"Alberto\"],\"propositions\":[],\"lastnames\":[\"Camacho\"],\"suffixes\":[]},{\"firstnames\":[\"Eleni\"],\"propositions\":[],\"lastnames\":[\"Triantafillou\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Jorge\"],\"propositions\":[],\"lastnames\":[\"Baier\"],\"suffixes\":[]},{\"firstnames\":[\"Sheila\",\"A.\"],\"propositions\":[],\"lastnames\":[\"McIlraith\"],\"suffixes\":[]}],\"booktitle\":\"Workshop on Heuristic Search and Domain Independent Planning (HSDIP'16)\",\"year\":\"2016\",\"url\":\"http://www.haz.ca/papers/camacho-hsdip16-ltl.pdf\",\"keywords\":\"non-deterministic planning, plan generation\",\"abstract\":\"Temporally extended goals are critical to the specification of a diversity of real-world planning problems. Here we examine the problem of planning with temporally extended goals over both finite and infinite traces where actions can be non-deterministic, and where temporally extended goals are specified in linear temporal logic (LTL). Unlike existing LTL planners, we place no restrictions on our LTL formulae beyond those necessary to distinguish finite from infinite trace interpretations. We realize our planner by translating LTL goals into either (Büchi) alternating or non-deterministic finite state automata, and exploiting a state-of-the-art fully observable non-deterministic planner to compute solutions. The resulting planner is sound and complete. Our system addresses a diverse spectrum of LTL planning problems that to this point had not been solvable using AI planning techniques. We do so while demonstrating competitive performance relative to the state of the art in LTL planning.\",\"bibtex\":\"@inproceedings{camacho-hsdip16-ltl,\\n  title = {LTL Synthesis for Non-Deterministic Systems on Finite and Infinite Traces},\\n  author = {Alberto Camacho and Eleni Triantafillou and Christian Muise and Jorge Baier and Sheila A. McIlraith},\\n  booktitle = {Workshop on Heuristic Search and Domain Independent Planning ({HSDIP}'16)},\\n  year = {2016},\\n  url = {http://www.haz.ca/papers/camacho-hsdip16-ltl.pdf},\\n  keywords = {non-deterministic planning, plan generation},\\n  abstract={Temporally extended goals are critical to the specification of a diversity of real-world planning problems. Here we examine the problem of planning with temporally extended goals over both finite and infinite traces where actions can be non-deterministic, and where temporally extended goals are specified in linear temporal logic (LTL). Unlike existing LTL planners, we place no restrictions on our LTL formulae beyond those necessary to distinguish finite from infinite trace interpretations. We realize our planner by translating LTL goals into either (Büchi) alternating or non-deterministic finite state automata, and exploiting a state-of-the-art fully observable non-deterministic planner to compute solutions. The resulting planner is sound and complete. Our system addresses a diverse spectrum of LTL planning problems that to this point had not been solvable using AI planning techniques. We do so while demonstrating competitive performance relative to the state of the art in LTL planning.}\\n}\\n\\n\",\"author_short\":[\"Camacho, A.\",\"Triantafillou, E.\",\"Muise, C.\",\"Baier, J.\",\"McIlraith, S. A.\"],\"key\":\"camacho-hsdip16-ltl\",\"id\":\"camacho-hsdip16-ltl\",\"bibbaseid\":\"camacho-triantafillou-muise-baier-mcilraith-ltlsynthesisfornondeterministicsystemsonfiniteandinfinitetraces-2016\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.haz.ca/papers/camacho-hsdip16-ltl.pdf\"},\"keyword\":[\"non-deterministic planning\",\"plan generation\"],\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Belief Update for Proper Epistemic Knowledge Bases\",\"author\":[{\"firstnames\":[\"Tim\"],\"propositions\":[],\"lastnames\":[\"Miller\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]}],\"booktitle\":\"International Joint Conference On Artificial Intelligence\",\"year\":\"2016\",\"url\":\"http://www.haz.ca/papers/miller-ijcai-16.pdf\",\"keywords\":\"multi-agent planning, epistemic reasoning\",\"abstract\":\"Reasoning about the nested beliefs of others is important in many multi-agent scenarios. While epistemic and doxastic logics lay a solid groundwork to approach such reasoning, the computational complexity of these logics is often too high for many tasks. Proper Epistemic Knowledge Bases (PEKBs) enforce two syntactic restrictions on formulae to obtain efficient querying: both disjunction and infinitely long nestings of modal operators are not permitted. PEKBs can be compiled, in exponential time, to a prime implicate formula that can be queried in polynomial time, while more recently, it was shown that consistent PEKBs had certain logical properties that meant this compilation was unnecessary, while still retaining polynomial-time querying. In this paper, we present a belief update mechanism for PEKBs that ensures the knowledge base remains consistent when new beliefs are added. This is achieved by first erasing any formulae that contradict these new beliefs. We show that this update mechanism can be computed in polynomial time, and we assess it against the well-known KM postulates for belief update.\",\"bibtex\":\"@inproceedings{miller-ijcai16,\\n  title = {Belief Update for Proper Epistemic Knowledge Bases},\\n  author = {Tim Miller and Christian Muise},\\n  booktitle = {International Joint Conference On Artificial Intelligence},\\n  year = {2016},\\n  url = {http://www.haz.ca/papers/miller-ijcai-16.pdf},\\n  keywords = {multi-agent planning, epistemic reasoning},\\n  abstract={Reasoning about the nested beliefs of others is important in many multi-agent scenarios. While epistemic and doxastic logics lay a solid groundwork to approach such reasoning, the computational complexity of these logics is often too high for many tasks. Proper Epistemic Knowledge Bases (PEKBs) enforce two syntactic restrictions on formulae to obtain efficient querying: both disjunction and infinitely long nestings of modal operators are not permitted. PEKBs can be compiled, in exponential time, to a prime implicate formula that can be queried in polynomial time, while more recently, it was shown that consistent PEKBs had certain logical properties that meant this compilation was unnecessary, while still retaining polynomial-time querying. In this paper, we present a belief update mechanism for PEKBs that ensures the knowledge base remains consistent when new beliefs are added. This is achieved by first erasing any formulae that contradict these new beliefs. We show that this update mechanism can be computed in polynomial time, and we assess it against the well-known KM postulates for belief update.}\\n}\\n\\n\",\"author_short\":[\"Miller, T.\",\"Muise, C.\"],\"key\":\"miller-ijcai16\",\"id\":\"miller-ijcai16\",\"bibbaseid\":\"miller-muise-beliefupdateforproperepistemicknowledgebases-2016\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.haz.ca/papers/miller-ijcai-16.pdf\"},\"keyword\":[\"multi-agent planning\",\"epistemic reasoning\"],\"metadata\":{\"authorlinks\":{\"miller, t\":\"https://uqtmiller.github.io/publications/\",\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":10,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Planning for a Single Agent in a Multi-Agent Environment Using FOND\",\"author\":[{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Paolo\"],\"propositions\":[],\"lastnames\":[\"Felli\"],\"suffixes\":[]},{\"firstnames\":[\"Tim\"],\"propositions\":[],\"lastnames\":[\"Miller\"],\"suffixes\":[]},{\"firstnames\":[\"Adrian\",\"R.\"],\"propositions\":[],\"lastnames\":[\"Pearce\"],\"suffixes\":[]},{\"firstnames\":[\"Liz\"],\"propositions\":[],\"lastnames\":[\"Sonenberg\"],\"suffixes\":[]}],\"booktitle\":\"International Joint Conference On Artificial Intelligence\",\"year\":\"2016\",\"url\":\"http://www.haz.ca/papers/muise-ijcai16.pdf\",\"keywords\":\"non-deterministic planning, multi-agent planning\",\"abstract\":\"Single-agent planning in a multi-agent environment is challenging because the actions of other agents can affect our ability to achieve a goal. From a given agent's perspective, actions of others can be viewed as non-deterministic outcomes of that agent's actions. While simple conceptually, this interpretation of planning in a multi-agent environment as non-deterministic planning remains challenging, not only due to the non-determinism resulting from others' actions, but because it is not clear how to compactly model the possible actions of others in the environment. In this paper, we cast the problem of planning in a multi-agent environment as one of Fully-Observable Non-Deterministic (FOND) planning. We extend a non-deterministic planner to plan in a multi-agent setting, allowing non-deterministic planning technology to solve a new class of planning problems. To improve the efficiency in domains too large for solving optimally, we propose a technique to use the goals and possible actions of other agents to focus the search on a set of plausible actions. We evaluate our approach on existing and new multi-agent benchmarks, demonstrating that modelling the other agents' goals improves the quality of the resulting solutions.\",\"bibtex\":\"@inproceedings{muise-ijcai16-mapasfond,\\n  title = {Planning for a Single Agent in a Multi-Agent Environment Using FOND},\\n  author = {Christian Muise and Paolo Felli and Tim Miller and Adrian R. Pearce and Liz Sonenberg},\\n  booktitle = {International Joint Conference On Artificial Intelligence},\\n  year = {2016},\\n  url = {http://www.haz.ca/papers/muise-ijcai16.pdf},\\n  keywords = {non-deterministic planning, multi-agent planning},\\n  abstract={Single-agent planning in a multi-agent environment is challenging because the actions of other agents can affect our ability to achieve a goal. From a given agent's perspective, actions of others can be viewed as non-deterministic outcomes of that agent's actions. While simple conceptually, this interpretation of planning in a multi-agent environment as non-deterministic planning remains challenging, not only due to the non-determinism resulting from others' actions, but because it is not clear how to compactly model the possible actions of others in the environment. In this paper, we cast the problem of planning in a multi-agent environment as one of Fully-Observable Non-Deterministic (FOND) planning. We extend a non-deterministic planner to plan in a multi-agent setting, allowing non-deterministic planning technology to solve a new class of planning problems. To improve the efficiency in domains too large for solving optimally, we propose a technique to use the goals and possible actions of other agents to focus the search on a set of plausible actions. We evaluate our approach on existing and new multi-agent benchmarks, demonstrating that modelling the other agents' goals improves the quality of the resulting solutions.}\\n}\\n\\n\",\"author_short\":[\"Muise, C.\",\"Felli, P.\",\"Miller, T.\",\"Pearce, A. R.\",\"Sonenberg, L.\"],\"key\":\"muise-ijcai16-mapasfond\",\"id\":\"muise-ijcai16-mapasfond\",\"bibbaseid\":\"muise-felli-miller-pearce-sonenberg-planningforasingleagentinamultiagentenvironmentusingfond-2016\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.haz.ca/papers/muise-ijcai16.pdf\"},\"keyword\":[\"non-deterministic planning\",\"multi-agent planning\"],\"metadata\":{\"authorlinks\":{\"miller, t\":\"https://uqtmiller.github.io/publications/\",\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":3,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Traps, Invariants, and Dead-Ends\",\"author\":[{\"firstnames\":[\"Nir\"],\"propositions\":[],\"lastnames\":[\"Lipovetzky\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Hector\"],\"propositions\":[],\"lastnames\":[\"Geffner\"],\"suffixes\":[]}],\"booktitle\":\"The 26th International Conference on Automated Planning and Scheduling\",\"year\":\"2016\",\"url\":\"http://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/13190\",\"keywords\":\"deadends, plan generation\",\"abstract\":\"We consider the problem of deriving formulas that capture traps, invariants, and dead-ends in classical planning through polynomial forms of preprocessing. An invariant is a formula that is true in the initial state and in all reachable states. A trap is a conditional invariant: once a state is reached that makes the trap true, all the states that are reachable from it will satisfy the trap formula as well. Finally, dead-ends are formulas that are satisfied in states that make the goal unreachable. We introduce a preprocessing algorithm that computes traps in k-DNF form that is exponential in the k parameter, and show how the algorithm can be used to precompute invariants and dead-ends. We report also preliminary tests that illustrate the effectiveness of the preprocessing algorithm for identifying dead-end states, and compare it with the identification that follows from the use of the $h^1$ and $h^2$ heuristics that cannot be preprocessed, and must be computed at run time.\",\"bibtex\":\"@inproceedings{lipovetzky-icaps16-trapper,\\n  title = {Traps, Invariants, and Dead-Ends},\\n  author = {Nir Lipovetzky and Christian Muise and Hector Geffner},\\n  booktitle = {The 26th International Conference on Automated Planning and Scheduling},\\n  year = {2016},\\n  url = {http://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/13190},\\n  keywords = {deadends, plan generation},\\n  abstract={We consider the problem of deriving formulas that capture traps, invariants, and dead-ends in classical planning through polynomial forms of preprocessing. An invariant is a formula that is true in the initial state and in all reachable states. A trap is a conditional invariant: once a state is reached that makes the trap true, all the states that are reachable from it will satisfy the trap formula as well. Finally, dead-ends are formulas that are satisfied in states that make the goal unreachable. We introduce a preprocessing algorithm that computes traps in k-DNF form that is exponential in the k parameter, and show how the algorithm can be used to precompute invariants and dead-ends. We report also preliminary tests that illustrate the effectiveness of the preprocessing algorithm for identifying dead-end states, and compare it with the identification that follows from the use of the $h^1$ and $h^2$ heuristics that cannot be preprocessed, and must be computed at run time.}\\n}\\n\\n\",\"author_short\":[\"Lipovetzky, N.\",\"Muise, C.\",\"Geffner, H.\"],\"key\":\"lipovetzky-icaps16-trapper\",\"id\":\"lipovetzky-icaps16-trapper\",\"bibbaseid\":\"lipovetzky-muise-geffner-trapsinvariantsanddeadends-2016\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/13190\"},\"keyword\":[\"deadends\",\"plan generation\"],\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"From FOND to Robust Probabilistic Planning: Computing compact policies that bypass avoidable deadends\",\"author\":[{\"firstnames\":[\"Alberto\"],\"propositions\":[],\"lastnames\":[\"Camacho\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Sheila\",\"A.\"],\"propositions\":[],\"lastnames\":[\"McIlraith\"],\"suffixes\":[]}],\"booktitle\":\"The 26th International Conference on Automated Planning and Scheduling\",\"year\":\"2016\",\"url\":\"http://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/13188\",\"keywords\":\"non-deterministic planning, probabilistic planning, plan generation\",\"abstract\":\"We address the class of probabilistic planning problems where the objective is to maximize the probability of reaching a prescribed goal. The complexity of probabilistic planning problems makes it difficult to compute high quality solutions for large instances, and existing algorithms either do not scale, or do so at the expense of the solution quality. We leverage core similarities between probabilistic and fully observable non-deterministic (FOND) planning to construct a sound, offline probabilistic planner, ProbPRP, that exploits algorithmic advances from state-of-the-art FOND planner, PRP, to compute compact policies that are guaranteed to bypass avoidable deadends. We evaluate ProbPRP on a selection of benchmarks used in past probabilistic planning competitions. The results show that ProbPRP, in many cases, outperforms the state of the art, computing substantially more robust policies and at times doing so orders of magnitude faster.\",\"bibtex\":\"@inproceedings{camacho-icaps16-probprp,\\n  title = {From FOND to Robust Probabilistic Planning: Computing compact policies that bypass avoidable deadends},\\n  author = {Alberto Camacho and Christian Muise and Sheila A. McIlraith},\\n  booktitle = {The 26th International Conference on Automated Planning and Scheduling},\\n  year = {2016},\\n  url = {http://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/13188},\\n  keywords = {non-deterministic planning, probabilistic planning, plan generation},\\n  abstract={We address the class of probabilistic planning problems where the objective is to maximize the probability of reaching a prescribed goal. The complexity of probabilistic planning problems makes it difficult to compute high quality solutions for large instances, and existing algorithms either do not scale, or do so at the expense of the solution quality. We leverage core similarities between probabilistic and fully observable non-deterministic (FOND) planning to construct a sound, offline probabilistic planner, ProbPRP, that exploits algorithmic advances from state-of-the-art FOND planner, PRP, to compute compact policies that are guaranteed to bypass avoidable deadends. We evaluate ProbPRP on a selection of benchmarks used in past probabilistic planning competitions. The results show that ProbPRP, in many cases, outperforms the state of the art, computing substantially more robust policies and at times doing so orders of magnitude faster.}\\n}\\n\\n\",\"author_short\":[\"Camacho, A.\",\"Muise, C.\",\"McIlraith, S. A.\"],\"key\":\"camacho-icaps16-probprp\",\"id\":\"camacho-icaps16-probprp\",\"bibbaseid\":\"camacho-muise-mcilraith-fromfondtorobustprobabilisticplanningcomputingcompactpoliciesthatbypassavoidabledeadends-2016\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/13188\"},\"keyword\":[\"non-deterministic planning\",\"probabilistic planning\",\"plan generation\"],\"metadata\":{\"authorlinks\":{\"camacho, a\":\"http://www.albertocamacho.com/\",\"mcilraith, s\":\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\",\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":6,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"firstnames\":[\"Tim\"],\"propositions\":[],\"lastnames\":[\"Miller\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Paolo\"],\"propositions\":[],\"lastnames\":[\"Felli\"],\"suffixes\":[]},{\"firstnames\":[\"Adrian\",\"R.\"],\"propositions\":[],\"lastnames\":[\"Pearce\"],\"suffixes\":[]},{\"firstnames\":[\"Liz\"],\"propositions\":[],\"lastnames\":[\"Sonenberg\"],\"suffixes\":[]}],\"year\":\"2016\",\"booktitle\":\"The 30th AAAI Conference on Artificial Intelligence\",\"keywords\":\"multi-agent planning, epistemic reasoning\",\"url\":\"http://www.haz.ca/papers/miller-aaai-16.pdf\",\"title\":\"'Knowing Whether' in Proper Epistemic Knowledge Bases\",\"abstract\":\"Proper epistemic knowledge bases (PEKBs) are syntactic knowledge bases that use multi-agent epistemic logic to represent nested multi-agent knowledge and belief. PEKBs have certain syntactic restrictions that lead to desirable computational properties; primarily, a PEKB is a conjunction of modal literals, and therefore contains no disjunction. Sound entailment can be checked in polynomial time, and is complete for a large set of arbitrary formulae in logics Kn and KDn. In this paper, we extend PEKBs to deal with a restricted form of disjunction: ‘knowing whether’. An agent i knows whether ϕ iff agent i knows ϕ or knows ¬ϕ; that is, iϕ ∨ i¬ϕ. In our experience, the ability to represent that an agent knows whether something holds is useful in many multi-agent domains. We represent knowing whether with a modal operator, ∆i, and present sound polynomial-time entailment algorithms on PEKBs with ∆i in Kn and KDn, but which are complete for a smaller class of queries than standard PEKBs.\",\"bibtex\":\"@inproceedings{miller-aaai-16,\\n  author = {Tim Miller and Christian Muise and Paolo Felli and Adrian R. Pearce and Liz Sonenberg},\\n  year = {2016},\\n  booktitle = {The 30th AAAI Conference on Artificial Intelligence},\\n  keywords = {multi-agent planning, epistemic reasoning},\\n  url = {http://www.haz.ca/papers/miller-aaai-16.pdf},\\n  title = {'Knowing Whether' in Proper Epistemic Knowledge Bases},\\n  abstract = {Proper epistemic knowledge bases (PEKBs) are syntactic knowledge bases that use multi-agent epistemic logic to represent nested multi-agent knowledge and belief. PEKBs have certain syntactic restrictions that lead to desirable computational properties; primarily, a PEKB is a conjunction of modal literals, and therefore contains no disjunction. Sound entailment can be checked in polynomial time, and is complete for a large set of arbitrary formulae in logics Kn and KDn. In this paper, we extend PEKBs to deal with a restricted form of disjunction: ‘knowing whether’. An agent i knows whether ϕ iff agent i knows ϕ or knows ¬ϕ; that is, iϕ ∨ i¬ϕ. In our experience, the ability to represent that an agent knows whether something holds is useful in many multi-agent domains. We represent knowing whether with a modal operator, ∆i, and present sound polynomial-time entailment algorithms on PEKBs with ∆i in Kn and KDn, but which are complete for a smaller class of queries than standard PEKBs.}\\n}\\n\\n\",\"author_short\":[\"Miller, T.\",\"Muise, C.\",\"Felli, P.\",\"Pearce, A. R.\",\"Sonenberg, L.\"],\"key\":\"miller-aaai-16\",\"id\":\"miller-aaai-16\",\"bibbaseid\":\"miller-muise-felli-pearce-sonenberg-knowingwhetherinproperepistemicknowledgebases-2016\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.haz.ca/papers/miller-aaai-16.pdf\"},\"keyword\":[\"multi-agent planning\",\"epistemic reasoning\"],\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":3,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"firstnames\":[\"Liz\"],\"propositions\":[],\"lastnames\":[\"Sonenberg\"],\"suffixes\":[]},{\"firstnames\":[\"Tim\"],\"propositions\":[],\"lastnames\":[\"Miller\"],\"suffixes\":[]},{\"firstnames\":[\"Adrian\"],\"propositions\":[],\"lastnames\":[\"Pearce\"],\"suffixes\":[]},{\"firstnames\":[\"Paolo\"],\"propositions\":[],\"lastnames\":[\"Felli\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Frank\"],\"propositions\":[],\"lastnames\":[\"Dignum\"],\"suffixes\":[]}],\"title\":\"Social planning for social HRI\",\"booktitle\":\"2nd Workshop on Cognitive Architectures for Social Human-Robot Interaction\",\"year\":\"2016\",\"keywords\":\"human-agent collaboration\",\"url\":\"http://arxiv.org/pdf/1602.06483v1.pdf\",\"bibtex\":\"@inproceedings{sonenberg-cashri16,\\n  author = {Liz Sonenberg and Tim Miller and Adrian Pearce and Paolo Felli and Christian Muise and Frank Dignum},\\n  title = {Social planning for social HRI},\\n  booktitle = {2nd Workshop on Cognitive Architectures for Social Human-Robot Interaction},\\n  year = {2016},\\n  keywords = {human-agent collaboration},\\n  url = {http://arxiv.org/pdf/1602.06483v1.pdf}\\n}\\n\\n\",\"author_short\":[\"Sonenberg, L.\",\"Miller, T.\",\"Pearce, A.\",\"Felli, P.\",\"Muise, C.\",\"Dignum, F.\"],\"key\":\"sonenberg-cashri16\",\"id\":\"sonenberg-cashri16\",\"bibbaseid\":\"sonenberg-miller-pearce-felli-muise-dignum-socialplanningforsocialhri-2016\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://arxiv.org/pdf/1602.06483v1.pdf\"},\"keyword\":[\"human-agent collaboration\"],\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Muise\"],\"firstnames\":[\"Christian\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"McIlraith\"],\"firstnames\":[\"Sheila\",\"A.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Beck\"],\"firstnames\":[\"J.\",\"Christopher\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Hsu\"],\"firstnames\":[\"Eric\"],\"suffixes\":[]}],\"booktitle\":\"AAAI-16 Workshop on Beyond NP\",\"title\":\"DSHARP: Fast d-DNNF Compilation with sharpSAT (Amended Version)\",\"keywords\":\"sat, knowledge compilation\",\"url\":\"http://haz.ca/dsharp-related.html\",\"year\":\"2016\",\"bibtex\":\"@inproceedings{muise-beyondnp16-dsharp,\\nauthor = {Muise, Christian and McIlraith, Sheila A. and Beck, J. Christopher and Hsu, Eric},\\nbooktitle = {AAAI-16 Workshop on Beyond NP},\\ntitle = {{DSHARP: Fast d-DNNF Compilation with sharpSAT (Amended Version)}},\\nkeywords = {sat, knowledge compilation},\\nurl = {http://haz.ca/dsharp-related.html},\\nyear = {2016}\\n}\\n\\n\",\"author_short\":[\"Muise, C.\",\"McIlraith, S. A.\",\"Beck, J. C.\",\"Hsu, E.\"],\"key\":\"muise-beyondnp16-dsharp\",\"id\":\"muise-beyondnp16-dsharp\",\"bibbaseid\":\"muise-mcilraith-beck-hsu-dsharpfastddnnfcompilationwithsharpsatamendedversion-2016\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://haz.ca/dsharp-related.html\"},\"keyword\":[\"sat\",\"knowledge compilation\"],\"metadata\":{\"authorlinks\":{\"mcilraith, s\":\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\",\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"firstnames\":[\"Rehan\",\"Abdul\"],\"propositions\":[],\"lastnames\":[\"Aziz\"],\"suffixes\":[]},{\"firstnames\":[\"Geoffrey\"],\"propositions\":[],\"lastnames\":[\"Chu\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Peter\"],\"propositions\":[],\"lastnames\":[\"Stuckey\"],\"suffixes\":[]}],\"year\":\"2015\",\"booktitle\":\"International Conference on Theory and Applications of Satisfiability Testing\",\"address\":\"Austin, USA\",\"keywords\":\"sat, knowledge compilation\",\"title\":\"Projected Model Counting\",\"url\":\"http://arxiv.org/pdf/1507.07648v1.pdf\",\"abstract\":\"Model counting is the task of computing the number of assignments to variables V that satisfy a given propositional theory F. The model counting problem is denoted as #SAT. Model counting is an essential tool in probabilistic reasoning. In this paper, we introduce the problem of model counting projected on a subset of original variables that we call priority variables P ⊆ V. The task is to compute the number of assignments to P such that there exists an extension to non-priority variables V \\\\ P that satisfies F. We denote this as #∃SAT. Projected model counting arises when some parts of the model are irrelevant to the counts, in particular when we require additional variables to model the problem we are counting in SAT. We discuss three different approaches to #∃SAT (two of which are novel), and compare their performance on different benchmark problems.\",\"bibtex\":\"@inproceedings{Aziz_CSM_15,\\n  author = {Rehan Abdul Aziz and Geoffrey Chu and Christian Muise and Peter Stuckey},\\n  year = {2015},\\n  booktitle = {International Conference on Theory and Applications of Satisfiability Testing},\\n  address = \\\"Austin, {USA}\\\",\\n  keywords = {sat, knowledge compilation},\\n  title = {Projected Model Counting},\\n  url = {http://arxiv.org/pdf/1507.07648v1.pdf},\\n  abstract = {Model counting is the task of computing the number of assignments to variables V that satisfy a given propositional theory F. The model counting problem is denoted as #SAT. Model counting is an essential tool in probabilistic reasoning. In this paper, we introduce the problem of model counting projected on a subset of original variables that we call priority variables P ⊆ V. The task is to compute the number of assignments to P such that there exists an extension to non-priority variables V \\\\ P that satisfies F. We denote this as #∃SAT. Projected model counting arises when some parts of the model are irrelevant to the counts, in particular when we require additional variables to model the problem we are counting in SAT. We discuss three different approaches to #∃SAT (two of which are novel), and compare their performance on different benchmark problems.}\\n}\\n\\n\",\"author_short\":[\"Aziz, R. A.\",\"Chu, G.\",\"Muise, C.\",\"Stuckey, P.\"],\"key\":\"Aziz_CSM_15\",\"id\":\"Aziz_CSM_15\",\"bibbaseid\":\"aziz-chu-muise-stuckey-projectedmodelcounting-2015\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://arxiv.org/pdf/1507.07648v1.pdf\"},\"keyword\":[\"sat\",\"knowledge compilation\"],\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"MAP-LAPKT: Omnipotent Multi-Agent Planning via Compilation to Classical Planning\",\"author\":[{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Nir\"],\"propositions\":[],\"lastnames\":[\"Lipovetzky\"],\"suffixes\":[]},{\"firstnames\":[\"Miquel\"],\"propositions\":[],\"lastnames\":[\"Ramirez\"],\"suffixes\":[]}],\"booktitle\":\"Competition of Distributed and Multiagent Planners\",\"year\":\"2015\",\"keywords\":\"multi-agent planning\",\"url\":\"http://www.haz.ca/papers/muise_CoDMAP15.pdf\",\"bibtex\":\"@inproceedings{muise-codmap15,\\n  title = {{MAP-LAPKT}: Omnipotent Multi-Agent Planning via Compilation to Classical Planning},\\n  author = {Christian Muise and Nir Lipovetzky and Miquel Ramirez},\\n  booktitle = {Competition of Distributed and Multiagent Planners},\\n  year = {2015},\\n  keywords = {multi-agent planning},\\n  url = {http://www.haz.ca/papers/muise_CoDMAP15.pdf}\\n}\\n\\n\",\"author_short\":[\"Muise, C.\",\"Lipovetzky, N.\",\"Ramirez, M.\"],\"key\":\"muise-codmap15\",\"id\":\"muise-codmap15\",\"bibbaseid\":\"muise-lipovetzky-ramirez-maplapktomnipotentmultiagentplanningviacompilationtoclassicalplanning-2015\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.haz.ca/papers/muise_CoDMAP15.pdf\"},\"keyword\":[\"multi-agent planning\"],\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Unplannability IPC Track\",\"author\":[{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Nir\"],\"propositions\":[],\"lastnames\":[\"Lipovetzky\"],\"suffixes\":[]}],\"booktitle\":\"Workshop on the International Planning Competition\",\"year\":\"2015\",\"keywords\":\"unsolvability,deadends\",\"url\":\"http://www.haz.ca/papers/muise_WIPC15.pdf\",\"abstract\":\"The majority of research in the field of automated planning focuses on the synthesis of plans for problems that are solvable. We propose an IPC track to focus on the important and understudied area of unplannibility: proving that a planning problem is unsolvable. We will focus on classical planning problems, as methods for determining whether or not unplannability can have wider applications for classical planning problems (e.g., recognizing and avoiding deadends in the state space) as well as solving planning problems with uncertainty (e.g., identifying when a deterministic approximation of the problem is unsolvable). The unplannability track follows similar contests in other fields; for example, the UNSAT track for the field of Boolean Satisfiability. In a similar vein, we hope that the introduction of an unplannability track will foster new innovation for techniques dedicated to identifying planning problems that cannot be solved.\",\"bibtex\":\"@inproceedings{muise-wipc15,\\n  title = {Unplannability IPC Track},\\n  author = {Christian Muise and Nir Lipovetzky},\\n  booktitle = {Workshop on the International Planning Competition},\\n  year = {2015},\\n  keywords = {unsolvability,deadends},\\n  url = {http://www.haz.ca/papers/muise_WIPC15.pdf},\\n  abstract = {The majority of research in the field of automated planning focuses on the synthesis of plans for problems that are solvable. We propose an IPC track to focus on the important and understudied area of unplannibility: proving that a planning problem is unsolvable. We will focus on classical planning problems, as methods for determining whether or not unplannability can have wider applications for classical planning problems (e.g., recognizing and avoiding deadends in the state space) as well as solving planning problems with uncertainty (e.g., identifying when a deterministic approximation of the problem is unsolvable). The unplannability track follows similar contests in other fields; for example, the UNSAT track for the field of Boolean Satisfiability. In a similar vein, we hope that the introduction of an unplannability track will foster new innovation for techniques dedicated to identifying planning problems that cannot be solved.}\\n}\\n\\n\",\"author_short\":[\"Muise, C.\",\"Lipovetzky, N.\"],\"key\":\"muise-wipc15\",\"id\":\"muise-wipc15\",\"bibbaseid\":\"muise-lipovetzky-unplannabilityipctrack-2015\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.haz.ca/papers/muise_WIPC15.pdf\"},\"keyword\":[\"unsolvability\",\"deadends\"],\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Towards Team Formation via Automated Planning\",\"author\":[{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Frank\"],\"propositions\":[],\"lastnames\":[\"Dignum\"],\"suffixes\":[]},{\"firstnames\":[\"Paolo\"],\"propositions\":[],\"lastnames\":[\"Felli\"],\"suffixes\":[]},{\"firstnames\":[\"Tim\"],\"propositions\":[],\"lastnames\":[\"Miller\"],\"suffixes\":[]},{\"firstnames\":[\"Adrian\",\"R.\"],\"propositions\":[],\"lastnames\":[\"Pearce\"],\"suffixes\":[]},{\"firstnames\":[\"Liz\"],\"propositions\":[],\"lastnames\":[\"Sonenberg\"],\"suffixes\":[]}],\"booktitle\":\"International Workshop on Coordination, Organisation, Institutions and Norms in Multi-Agent Systems\",\"year\":\"2015\",\"keywords\":\"multi-agent planning, epistemic reasoning\",\"url\":\"http://www.haz.ca/papers/muise-coinATijcai-15.pdf\",\"abstract\":\"Cooperative problem solving involves four key phases: (1) finding potential members to form a team, (2) forming the team, (3) formulating a plan for the team, and (4) executing the plan. We extend recent work on multi-agent epistemic planning and apply it to the problem of team formation in a blocksworld scenario. We provide an encoding of the first three phases of team formation from the perspective of an initiator, and show how automated planning efficiently yields conditional plans that guarantee certain collective intentions will be achieved. The expressiveness of the epistemic planning formalism, which supports modelling with the nested beliefs of agents, opens the prospect of broad applicability to the operationalisation of collective intention.\",\"bibtex\":\"@inproceedings{muise-coin15-teamwork,\\n  title = {Towards Team Formation via Automated Planning},\\n  author = {Christian Muise and Frank Dignum and Paolo Felli and Tim Miller and Adrian R. Pearce and Liz Sonenberg},\\n  booktitle = {International Workshop on Coordination, Organisation, Institutions and Norms in Multi-Agent Systems},\\n  year = {2015},\\n  keywords = {multi-agent planning, epistemic reasoning},\\n  url = {http://www.haz.ca/papers/muise-coinATijcai-15.pdf},\\n  abstract={Cooperative problem solving involves four key phases: (1) finding potential members to form a team, (2) forming the team, (3) formulating a plan for the team, and (4) executing the plan. We extend recent work on multi-agent epistemic planning and apply it to the problem of team formation in a blocksworld scenario. We provide an encoding of the first three phases of team formation from the perspective of an initiator, and show how automated planning efficiently yields conditional plans that guarantee certain collective intentions will be achieved. The expressiveness of the epistemic planning formalism, which supports modelling with the nested beliefs of agents, opens the prospect of broad applicability to the operationalisation of collective intention.}\\n}\\n\\n\\n\",\"author_short\":[\"Muise, C.\",\"Dignum, F.\",\"Felli, P.\",\"Miller, T.\",\"Pearce, A. R.\",\"Sonenberg, L.\"],\"key\":\"muise-coin15-teamwork\",\"id\":\"muise-coin15-teamwork\",\"bibbaseid\":\"muise-dignum-felli-miller-pearce-sonenberg-towardsteamformationviaautomatedplanning-2015\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.haz.ca/papers/muise-coinATijcai-15.pdf\"},\"keyword\":[\"multi-agent planning\",\"epistemic reasoning\"],\"metadata\":{\"authorlinks\":{\"miller, t\":\"https://uqtmiller.github.io/publications/\",\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"From FOND to Probabilistic Planning: Guiding search for quality policies\",\"author\":[{\"firstnames\":[\"Alberto\"],\"propositions\":[],\"lastnames\":[\"Camacho\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Akshay\"],\"propositions\":[],\"lastnames\":[\"Ganeshen\"],\"suffixes\":[]},{\"firstnames\":[\"Sheila\",\"A.\"],\"propositions\":[],\"lastnames\":[\"McIlraith\"],\"suffixes\":[]}],\"booktitle\":\"Workshop on Heuristic Search and Domain Independent Planning (HSDIP'15)\",\"year\":\"2015\",\"keywords\":\"non-deterministic planning, probabilistic planning, plan generation\",\"url\":\"http://www.haz.ca/papers/camacho-hsdip15-probprp.pdf\",\"abstract\":\"We address the class of probabilistic planning problems where the objective is to maximize the probability of reaching a prescribed goal (MAXPROB). State-of-the-art probabilistic planners, and in particular MAXPROB planners, offer few guarantees with respect to the quality or optimality of the solutions that they find. The complexity of MAXPROB problems makes it difficult to compute high quality solutions for big problems, and existing algorithms either do not scale well, or provide poor quality solutions. We exploit core similarities between probabilistic and fully observable non-deterministic (FOND) planning models to extend the state-of-the-art FOND planner, PRP, to be a sound and sometimes complete MAXPROB solver that is guaranteed to sidestep avoidable deadends. We evaluate our planner, ProbPRP, on a selection of benchmarks used in past probabilistic planning competitions. The results show that ProbPRP outperforms previous state-of-the-art algorithms for solving MAXPROB, and computes substantially more robust policies, at times doing so orders of magnitude faster.\",\"bibtex\":\"@inproceedings{camacho-hsdip15-probprp,\\n  title = {From FOND to Probabilistic Planning: Guiding search for quality policies},\\n  author = {Alberto Camacho and Christian Muise and Akshay Ganeshen and Sheila A. McIlraith},\\n  booktitle = {Workshop on Heuristic Search and Domain Independent Planning ({HSDIP}'15)},\\n  year = {2015},\\n  keywords = {non-deterministic planning, probabilistic planning, plan generation},\\n  url={http://www.haz.ca/papers/camacho-hsdip15-probprp.pdf},\\n  abstract={We address the class of probabilistic planning problems where the objective is to maximize the probability of reaching a prescribed goal (MAXPROB). State-of-the-art probabilistic planners, and in particular MAXPROB planners, offer few guarantees with respect to the quality or optimality of the solutions that they find. The complexity of MAXPROB problems makes it difficult to compute high quality solutions for big problems, and existing algorithms either do not scale well, or provide poor quality solutions. We exploit core similarities between probabilistic and fully observable non-deterministic (FOND) planning models to extend the state-of-the-art FOND planner, PRP, to be a sound and sometimes complete MAXPROB solver that is guaranteed to sidestep avoidable deadends. We evaluate our planner, ProbPRP, on a selection of benchmarks used in past probabilistic planning competitions. The results show that ProbPRP outperforms previous state-of-the-art algorithms for solving MAXPROB, and computes substantially more robust policies, at times doing so orders of magnitude faster.}\\n}\\n\\n\",\"author_short\":[\"Camacho, A.\",\"Muise, C.\",\"Ganeshen, A.\",\"McIlraith, S. A.\"],\"key\":\"camacho-hsdip15-probprp\",\"id\":\"camacho-hsdip15-probprp\",\"bibbaseid\":\"camacho-muise-ganeshen-mcilraith-fromfondtoprobabilisticplanningguidingsearchforqualitypolicies-2015\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.haz.ca/papers/camacho-hsdip15-probprp.pdf\"},\"keyword\":[\"non-deterministic planning\",\"probabilistic planning\",\"plan generation\"],\"metadata\":{\"authorlinks\":{\"camacho, a\":\"http://www.albertocamacho.com/\",\"mcilraith, s\":\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\",\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Planning Over Multi-Agent Epistemic States: A Classical Planning Approach (Amended Version)\",\"author\":[{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Vaishak\"],\"propositions\":[],\"lastnames\":[\"Belle\"],\"suffixes\":[]},{\"firstnames\":[\"Paolo\"],\"propositions\":[],\"lastnames\":[\"Felli\"],\"suffixes\":[]},{\"firstnames\":[\"Sheila\",\"A.\"],\"propositions\":[],\"lastnames\":[\"McIlraith\"],\"suffixes\":[]},{\"firstnames\":[\"Tim\"],\"propositions\":[],\"lastnames\":[\"Miller\"],\"suffixes\":[]},{\"firstnames\":[\"Adrian\",\"R.\"],\"propositions\":[],\"lastnames\":[\"Pearce\"],\"suffixes\":[]},{\"firstnames\":[\"Liz\"],\"propositions\":[],\"lastnames\":[\"Sonenberg\"],\"suffixes\":[]}],\"booktitle\":\"Workshop on Distributed and Multi-Agent Planning (DMAP'15)\",\"year\":\"2015\",\"keywords\":\"multi-agent planning, epistemic reasoning\",\"url\":\"http://www.haz.ca/papers/muise-dmap15-pdkbplanning.pdf\",\"abstract\":\"Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology. Our approach represents an important first step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents.\",\"bibtex\":\"@inproceedings{muise-dmap15-pdkbplanning,\\n  title = {Planning Over Multi-Agent Epistemic States: A Classical Planning Approach (Amended Version)},\\n  author = {Christian Muise and Vaishak Belle and Paolo Felli and Sheila A. McIlraith and Tim Miller and Adrian R. Pearce and Liz Sonenberg},\\n  booktitle = {Workshop on Distributed and Multi-Agent Planning ({DMAP}'15)},\\n  year = {2015},\\n  keywords = {multi-agent planning, epistemic reasoning},\\n  url={http://www.haz.ca/papers/muise-dmap15-pdkbplanning.pdf},\\n  abstract={Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology. Our approach represents an important first step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents.}\\n}\\n\\n\",\"author_short\":[\"Muise, C.\",\"Belle, V.\",\"Felli, P.\",\"McIlraith, S. A.\",\"Miller, T.\",\"Pearce, A. R.\",\"Sonenberg, L.\"],\"key\":\"muise-dmap15-pdkbplanning\",\"id\":\"muise-dmap15-pdkbplanning\",\"bibbaseid\":\"muise-belle-felli-mcilraith-miller-pearce-sonenberg-planningovermultiagentepistemicstatesaclassicalplanningapproachamendedversion-2015\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.haz.ca/papers/muise-dmap15-pdkbplanning.pdf\"},\"keyword\":[\"multi-agent planning\",\"epistemic reasoning\"],\"metadata\":{\"authorlinks\":{\"mcilraith, s\":\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\",\"muise, c\":\"https://www.haz.ca/academic-publications.html\",\"belle, v\":\"https://bibbase.org/show?bib=https://dblp.org/pid/52/570.bib\"}},\"downloads\":6,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Leveraging FOND Planning Technology to Solve Multi-Agent Planning Problems\",\"author\":[{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Paolo\"],\"propositions\":[],\"lastnames\":[\"Felli\"],\"suffixes\":[]},{\"firstnames\":[\"Tim\"],\"propositions\":[],\"lastnames\":[\"Miller\"],\"suffixes\":[]},{\"firstnames\":[\"Adrian\",\"R.\"],\"propositions\":[],\"lastnames\":[\"Pearce\"],\"suffixes\":[]},{\"firstnames\":[\"Liz\"],\"propositions\":[],\"lastnames\":[\"Sonenberg\"],\"suffixes\":[]}],\"booktitle\":\"Workshop on Distributed and Multi-Agent Planning (DMAP'15)\",\"year\":\"2015\",\"keywords\":\"non-deterministic planning, multi-agent planning\",\"url\":\"http://www.haz.ca/papers/muise-dmap15-mapasfond.pdf\",\"abstract\":\"Single-agent planning in a multi-agent environment is challenging because the actions of other agents can affect our ability to achieve a goal. From a given agent's perspective, actions of others can be viewed as non-deterministic outcomes of that agent's actions. While simple conceptually, this interpretation of planning in a multi-agent environment as non-deterministic planning is challenging due to the non-determinism resulting from others' actions, and because it is not clear how to compactly model the possible actions of others in the environment. In this paper, we cast the problem of planning in a multi-agent environment as one of Fully-Observable Non-Deterministic (FOND) planning. We extend a non-deterministic planner to plan in a multi-agent setting, given the goals and possible actions of other agents. We use the other agents' goals to reduce their set of possible actions to a set of plausible actions, allowing non-deterministic planning technology to solve a new class of planning problems in first-person multi-agent environments. We demonstrate our approach on new and existing multi-agent benchmarks, demonstrating that modelling the other agents' goals reduces complexity.\",\"bibtex\":\"@inproceedings{muise-dmap15-mapasfond,\\n  title = {Leveraging FOND Planning Technology to Solve Multi-Agent Planning Problems},\\n  author = {Christian Muise and Paolo Felli and Tim Miller and Adrian R. Pearce and Liz Sonenberg},\\n  booktitle = {Workshop on Distributed and Multi-Agent Planning ({DMAP}'15)},\\n  year = {2015},\\n  keywords = {non-deterministic planning, multi-agent planning},\\n  url={http://www.haz.ca/papers/muise-dmap15-mapasfond.pdf},\\n  abstract={Single-agent planning in a multi-agent environment is challenging because the actions of other agents can affect our ability to achieve a goal. From a given agent's perspective, actions of others can be viewed as non-deterministic outcomes of that agent's actions. While simple conceptually, this interpretation of planning in a multi-agent environment as non-deterministic planning is challenging due to the non-determinism resulting from others' actions, and because it is not clear how to compactly model the possible actions of others in the environment. In this paper, we cast the problem of planning in a multi-agent environment as one of Fully-Observable Non-Deterministic (FOND) planning. We extend a non-deterministic planner to plan in a multi-agent setting, given the goals and possible actions of other agents. We use the other agents' goals to reduce their set of possible actions to a set of plausible actions, allowing non-deterministic planning technology to solve a new class of planning problems in first-person multi-agent environments. We demonstrate our approach on new and existing multi-agent benchmarks, demonstrating that modelling the other agents' goals reduces complexity.}\\n}\\n\\n\",\"author_short\":[\"Muise, C.\",\"Felli, P.\",\"Miller, T.\",\"Pearce, A. R.\",\"Sonenberg, L.\"],\"key\":\"muise-dmap15-mapasfond\",\"id\":\"muise-dmap15-mapasfond\",\"bibbaseid\":\"muise-felli-miller-pearce-sonenberg-leveragingfondplanningtechnologytosolvemultiagentplanningproblems-2015\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.haz.ca/papers/muise-dmap15-mapasfond.pdf\"},\"keyword\":[\"non-deterministic planning\",\"multi-agent planning\"],\"metadata\":{\"authorlinks\":{\"miller, t\":\"https://uqtmiller.github.io/publications/\",\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Computing Social Behaviours Using Agent Models\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Felli\"],\"firstnames\":[\"Paolo\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Miller\"],\"firstnames\":[\"Tim\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Muise\"],\"firstnames\":[\"Christian\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pearce\"],\"firstnames\":[\"Adrian\",\"R.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Sonenberg\"],\"firstnames\":[\"Liz\"],\"suffixes\":[]}],\"booktitle\":\"International Joint Conference on Artificial Intelligence, IJCAI 2015\",\"year\":\"2015\",\"keywords\":\"human-agent collaboration\",\"url\":\"http://www.haz.ca/papers/felli-ijcai15.pdf\",\"abstract\":\"Agents can be thought of as following a social behaviour, depending on the context in which they are interacting. We devise a computationally grounded mechanism to represent and reason about others in social terms, reflecting the local perspective of an agent (first-person view), to support both stereotypical and empathetic reasoning. We use a hierarchy of agent models to discriminate which behaviours of others are plausible, and decide which behaviour for ourselves is socially acceptable, i.e. conforms to the social context. To this aim, we investigate the implications of considering agents capable of various degrees of Theory of Mind, and discuss a scenario showing how this affects behaviour.\",\"bibtex\":\"@inproceedings{felli-ijcai-2015,\\n  title = {Computing Social Behaviours Using Agent Models},\\n  author = {Felli, Paolo and Miller, Tim and Muise, Christian and Pearce, Adrian R. and Sonenberg, Liz},\\n  booktitle = {International Joint Conference on Artificial Intelligence, {IJCAI} 2015},\\n  year = {2015},\\n  keywords = {human-agent collaboration},\\n  url={http://www.haz.ca/papers/felli-ijcai15.pdf},\\n  abstract = {Agents can be thought of as following a social behaviour, depending on the context in which they are interacting. We devise a computationally grounded mechanism to represent and reason about others in social terms, reflecting the local perspective of an agent (first-person view), to support both stereotypical and empathetic reasoning. We use a hierarchy of agent models to discriminate which behaviours of others are plausible, and decide which behaviour for ourselves is socially acceptable, i.e. conforms to the social context. To this aim, we investigate the implications of considering agents capable of various degrees of Theory of Mind, and discuss a scenario showing how this affects behaviour.}\\n}\\n\\n\",\"author_short\":[\"Felli, P.\",\"Miller, T.\",\"Muise, C.\",\"Pearce, A. R.\",\"Sonenberg, L.\"],\"key\":\"felli-ijcai-2015\",\"id\":\"felli-ijcai-2015\",\"bibbaseid\":\"felli-miller-muise-pearce-sonenberg-computingsocialbehavioursusingagentmodels-2015\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.haz.ca/papers/felli-ijcai15.pdf\"},\"keyword\":[\"human-agent collaboration\"],\"metadata\":{\"authorlinks\":{\"miller, t\":\"https://uqtmiller.github.io/publications/\",\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"abstract\":\"Reasoning about the nested beliefs or knowledge of other agents is essential for many collaborative and competitive tasks. However, reasoning with nested belief (for example through epistemic logics) is computationally expensive. Proper Epistemic Knowledge Bases (PEKBs) address this by enforcing syntactic restrictions on the knowledge base. By compiling a PEKB and query formula into a specific normal form, entailment can be checked in polynomial time, which is sound and complete for the epistemic logic Kn. The downside is that the complexity of compiling into the normal form is exponential in time and space. In this work, we extend PEKBs to handle belief in the logic of Kd. We show that this simplifies the complexity of the required reasoning, and importantly, achieves polynomial entailment checking without first having to compile the PEKB into a normal form. Also, we present an alternative approach that calculates the closure of a PEKB, which is exponential in the maximum depth of nested belief, but for which entailment checking is constant on average.\",\"title\":\"Efficient Reasoning With Consistent Proper Epistemic Knowledge Bases\",\"author\":[{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Tim\"],\"propositions\":[],\"lastnames\":[\"Miller\"],\"suffixes\":[]},{\"firstnames\":[\"Paolo\"],\"propositions\":[],\"lastnames\":[\"Felli\"],\"suffixes\":[]},{\"firstnames\":[\"Adrian\",\"R.\"],\"propositions\":[],\"lastnames\":[\"Pearce\"],\"suffixes\":[]},{\"firstnames\":[\"Liz\"],\"propositions\":[],\"lastnames\":[\"Sonenberg\"],\"suffixes\":[]}],\"booktitle\":\"The International Conference on Autonomous Agents and Multiagent Systems\",\"year\":\"2015\",\"keywords\":\"multi-agent planning, epistemic reasoning\",\"url\":\"http://www.haz.ca/papers/muise-aamas-15.pdf\",\"bibtex\":\"@inproceedings{muise-aamas-15,\\n  abstract={Reasoning about the nested beliefs or knowledge of other agents is essential for many collaborative and competitive tasks. However, reasoning with nested belief (for example through epistemic logics) is computationally expensive. Proper Epistemic Knowledge Bases (PEKBs) address this by enforcing syntactic restrictions on the knowledge base. By compiling a PEKB and query formula into a specific normal form, entailment can be checked in polynomial time, which is sound and complete for the epistemic logic Kn. The downside is that the complexity of compiling into the normal form is exponential in time and space. In this work, we extend PEKBs to handle belief in the logic of Kd. We show that this simplifies the complexity of the required reasoning, and importantly, achieves polynomial entailment checking without first having to compile the PEKB into a normal form. Also, we present an alternative approach that calculates the closure of a PEKB, which is exponential in the maximum depth of nested belief, but for which entailment checking is constant on average.},\\n  title={Efficient Reasoning With Consistent Proper Epistemic Knowledge Bases},\\n  author={Christian Muise and Tim Miller and Paolo Felli and Adrian R. Pearce and Liz Sonenberg},\\n  booktitle={The International Conference on Autonomous Agents and Multiagent Systems},\\n  year={2015},\\n  keywords = {multi-agent planning, epistemic reasoning},\\n  url={http://www.haz.ca/papers/muise-aamas-15.pdf}\\n}\\n\\n\",\"author_short\":[\"Muise, C.\",\"Miller, T.\",\"Felli, P.\",\"Pearce, A. R.\",\"Sonenberg, L.\"],\"key\":\"muise-aamas-15\",\"id\":\"muise-aamas-15\",\"bibbaseid\":\"muise-miller-felli-pearce-sonenberg-efficientreasoningwithconsistentproperepistemicknowledgebases-2015\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.haz.ca/papers/muise-aamas-15.pdf\"},\"keyword\":[\"multi-agent planning\",\"epistemic reasoning\"],\"metadata\":{\"authorlinks\":{\"miller, t\":\"https://uqtmiller.github.io/publications/\",\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":3,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"abstract\":\"Model counting is the problem of computing the number of models that satisfy a given propositional theory. It has recently been applied to solving inference tasks in probabilistic logic programming, where the goal is to compute the probability of given queries being true provided a set of mutually independent random variables, a model (a logic program) and some evidence. The core of solving this inference task involves translating the logic program to a propositional theory and using a model counter. In this paper, we show that for some problems that involve inductive definitions like reachability in a graph, the translation of logic programs to SAT can be expensive for the purpose of solving inference tasks. For such problems, direct implementation of stable model semantics allows for more efficient solving. We present two implementation techniques, based on unfounded set detection, that extend a propositional model counter to a stable model counter. Our experiments show that for particular problems, our approach can outperform a state-of-the-art probabilistic logic programming solver by several orders of magnitude in terms of running time and space requirements, and can solve instances of significantly larger sizes on which the current solver runs out of time or memory.\",\"title\":\"Stable model counting and its application in probabilistic logic programming\",\"author\":[{\"firstnames\":[\"Rehan\",\"Abdul\"],\"propositions\":[],\"lastnames\":[\"Aziz\"],\"suffixes\":[]},{\"firstnames\":[\"Geoffrey\"],\"propositions\":[],\"lastnames\":[\"Chu\"],\"suffixes\":[]},{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Peter\"],\"propositions\":[],\"lastnames\":[\"Stuckey\"],\"suffixes\":[]}],\"booktitle\":\"The 29th AAAI Conference on Artificial Intelligence\",\"keywords\":\"sat, knowledge compilation\",\"year\":\"2015\",\"url\":\"http://arxiv.org/pdf/1411.5410v1.pdf\",\"bibtex\":\"@inproceedings{aziz-aaai-15,\\n  abstract={Model counting is the problem of computing the number of models that satisfy a given propositional theory. It has recently been applied to solving inference tasks in probabilistic logic programming, where the goal is to compute the probability of given queries being true provided a set of mutually independent random variables, a model (a logic program) and some evidence. The core of solving this inference task involves translating the logic program to a propositional theory and using a model counter. In this paper, we show that for some problems that involve inductive definitions like reachability in a graph, the translation of logic programs to SAT can be expensive for the purpose of solving inference tasks. For such problems, direct implementation of stable model semantics allows for more efficient solving. We present two implementation techniques, based on unfounded set detection, that extend a propositional model counter to a stable model counter. Our experiments show that for particular problems, our approach can outperform a state-of-the-art probabilistic logic programming solver by several orders of magnitude in terms of running time and space requirements, and can solve instances of significantly larger sizes on which the current solver runs out of time or memory.},\\n  title={Stable model counting and its application in probabilistic logic programming},\\n  author={Rehan Abdul Aziz and Geoffrey Chu and Christian Muise and Peter Stuckey},\\n  booktitle={The 29th AAAI Conference on Artificial Intelligence},\\n  keywords = {sat, knowledge compilation},\\n  year={2015},\\n  url={http://arxiv.org/pdf/1411.5410v1.pdf}\\n}\\n\\n\",\"author_short\":[\"Aziz, R. A.\",\"Chu, G.\",\"Muise, C.\",\"Stuckey, P.\"],\"key\":\"aziz-aaai-15\",\"id\":\"aziz-aaai-15\",\"bibbaseid\":\"aziz-chu-muise-stuckey-stablemodelcountinganditsapplicationinprobabilisticlogicprogramming-2015\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://arxiv.org/pdf/1411.5410v1.pdf\"},\"keyword\":[\"sat\",\"knowledge compilation\"],\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"abstract\":\"Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology. Our approach represents an important first step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents.\",\"title\":\"Planning Over Multi-Agent Epistemic States: A Classical Planning Approach\",\"author\":[{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]},{\"firstnames\":[\"Vaishak\"],\"propositions\":[],\"lastnames\":[\"Belle\"],\"suffixes\":[]},{\"firstnames\":[\"Paolo\"],\"propositions\":[],\"lastnames\":[\"Felli\"],\"suffixes\":[]},{\"firstnames\":[\"Sheila\",\"A.\"],\"propositions\":[],\"lastnames\":[\"McIlraith\"],\"suffixes\":[]},{\"firstnames\":[\"Tim\"],\"propositions\":[],\"lastnames\":[\"Miller\"],\"suffixes\":[]},{\"firstnames\":[\"Adrian\",\"R.\"],\"propositions\":[],\"lastnames\":[\"Pearce\"],\"suffixes\":[]},{\"firstnames\":[\"Liz\"],\"propositions\":[],\"lastnames\":[\"Sonenberg\"],\"suffixes\":[]}],\"booktitle\":\"The 29th AAAI Conference on Artificial Intelligence\",\"year\":\"2015\",\"keywords\":\"multi-agent planning, epistemic reasoning\",\"url\":\"http://www.haz.ca/papers/muise-aaai-15.pdf\",\"bibtex\":\"@inproceedings{muise-aaai-15,\\n  abstract={Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology. Our approach represents an important first step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents.},\\n  title={Planning Over Multi-Agent Epistemic States: A Classical Planning Approach},\\n  author={Christian Muise and Vaishak Belle and Paolo Felli and Sheila A. McIlraith and Tim Miller and Adrian R. Pearce and Liz Sonenberg},\\n  booktitle={The 29th AAAI Conference on Artificial Intelligence},\\n  year={2015},\\n  keywords = {multi-agent planning, epistemic reasoning},\\n  url={http://www.haz.ca/papers/muise-aaai-15.pdf}\\n}\\n\\n\",\"author_short\":[\"Muise, C.\",\"Belle, V.\",\"Felli, P.\",\"McIlraith, S. A.\",\"Miller, T.\",\"Pearce, A. R.\",\"Sonenberg, L.\"],\"key\":\"muise-aaai-15\",\"id\":\"muise-aaai-15\",\"bibbaseid\":\"muise-belle-felli-mcilraith-miller-pearce-sonenberg-planningovermultiagentepistemicstatesaclassicalplanningapproach-2015\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.haz.ca/papers/muise-aaai-15.pdf\"},\"keyword\":[\"multi-agent planning\",\"epistemic reasoning\"],\"metadata\":{\"authorlinks\":{\"mcilraith, s\":\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\",\"miller, t\":\"https://uqtmiller.github.io/publications/\",\"muise, c\":\"https://www.haz.ca/academic-publications.html\",\"belle, v\":\"https://bibbase.org/show?bib=https://dblp.org/pid/52/570.bib\"}},\"downloads\":12,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"abstract\":\"With a view to supporting expressive, but tractable, collaborative interactions between humans and agents, we propose an approach for representing heterogeneous agent models, i.e., with potentially diverse mental abilities and holding stereotypical characteristics as members of a social reference group. We build a computationally grounded mechanism for progressing their beliefs about others' beliefs, supporting stereotypical as well as empathic reasoning. We comment on how this approach can be used to build finite-state games, restricting the analysis of possibly large-scale problems by focusing only on the set of plausible evolutions.\",\"title\":\"Artificial social reasoning: computational mechanisms for reasoning about others\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Felli\"],\"firstnames\":[\"Paolo\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Miller\"],\"firstnames\":[\"Tim\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Muise\"],\"firstnames\":[\"Christian\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pearce\"],\"firstnames\":[\"Adrian\",\"R.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Sonenberg\"],\"firstnames\":[\"Liz\"],\"suffixes\":[]}],\"booktitle\":\"The International Conference on Social Robotics\",\"keywords\":\"human-agent collaboration\",\"year\":\"2014\",\"url\":\"http://agentlab.cis.unimelb.edu.au/papers/felli-icsr-2014.pdf\",\"bibtex\":\"@inproceedings{felli-icsr-2014,\\n  abstract={With a view to supporting expressive, but tractable, collaborative interactions between humans and agents, we propose an approach for representing heterogeneous agent models, i.e., with potentially diverse mental abilities and holding stereotypical characteristics as members of a social reference group. We build a computationally grounded mechanism for progressing their beliefs about others' beliefs, supporting stereotypical as well as empathic reasoning. We comment on how this approach can be used to build finite-state games, restricting the analysis of possibly large-scale problems by focusing only on the set of plausible evolutions.},\\n  title={Artificial social reasoning: computational mechanisms for reasoning about others},\\n  author={Felli, Paolo and Miller, Tim and Muise, Christian and Pearce, Adrian R. and Sonenberg, Liz},\\n  booktitle={The International Conference on Social Robotics},\\n  keywords = {human-agent collaboration},\\n  year={2014},\\n  url={http://agentlab.cis.unimelb.edu.au/papers/felli-icsr-2014.pdf}\\n}\\n\\n\",\"author_short\":[\"Felli, P.\",\"Miller, T.\",\"Muise, C.\",\"Pearce, A. R.\",\"Sonenberg, L.\"],\"key\":\"felli-icsr-2014\",\"id\":\"felli-icsr-2014\",\"bibbaseid\":\"felli-miller-muise-pearce-sonenberg-artificialsocialreasoningcomputationalmechanismsforreasoningaboutothers-2014\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://agentlab.cis.unimelb.edu.au/papers/felli-icsr-2014.pdf\"},\"keyword\":[\"human-agent collaboration\"],\"metadata\":{\"authorlinks\":{\"miller, t\":\"https://uqtmiller.github.io/publications/\",\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":67,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Foundations of Human-Agent Collaboration: Situation-Relevant Information Sharing\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Miller\"],\"firstnames\":[\"Tim\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pearce\"],\"firstnames\":[\"Adrian\",\"R.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Sonenberg\"],\"firstnames\":[\"Liz\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Dignum\"],\"firstnames\":[\"Frank\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Felli\"],\"firstnames\":[\"Paolo\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Muise\"],\"firstnames\":[\"Christian\"],\"suffixes\":[]}],\"booktitle\":\"The AAAI 2014 Fall Symposium on AI for Human-Robot Interaction\",\"year\":\"2014\",\"keywords\":\"human-agent collaboration\",\"url\":\"http://agentlab.cis.unimelb.edu.au/papers/miller-aaai-symposium-2014.pdf\",\"note\":\"Extended Abstract\",\"bibtex\":\"@inproceedings{miller-aaai-symposium-2014,\\n  title={Foundations of Human-Agent Collaboration: Situation-Relevant Information Sharing},\\n  author={Miller, Tim and Pearce, Adrian R. and Sonenberg, Liz and Dignum, Frank and Felli, Paolo and Muise, Christian},\\n  booktitle={The AAAI 2014 Fall Symposium on AI for Human-Robot Interaction},\\n  year={2014},\\n  keywords = {human-agent collaboration},\\n  url={http://agentlab.cis.unimelb.edu.au/papers/miller-aaai-symposium-2014.pdf},\\n  note = {Extended Abstract}\\n}\\n\\n\",\"author_short\":[\"Miller, T.\",\"Pearce, A. R.\",\"Sonenberg, L.\",\"Dignum, F.\",\"Felli, P.\",\"Muise, C.\"],\"key\":\"miller-aaai-symposium-2014\",\"id\":\"miller-aaai-symposium-2014\",\"bibbaseid\":\"miller-pearce-sonenberg-dignum-felli-muise-foundationsofhumanagentcollaborationsituationrelevantinformationsharing-2014\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://agentlab.cis.unimelb.edu.au/papers/miller-aaai-symposium-2014.pdf\"},\"keyword\":[\"human-agent collaboration\"],\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":14,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Width and Inference Based Planners: SIW, BFS (f), and PROBE\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Lipovetzky\"],\"firstnames\":[\"Nir\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ramirez\"],\"firstnames\":[\"Miquel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Muise\"],\"firstnames\":[\"Christian\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Geffner\"],\"firstnames\":[\"Hector\"],\"suffixes\":[]}],\"url\":\"http://people.eng.unimelb.edu.au/nlipovetzky/papers/ipc8_siw_bfs_probe.pdf\",\"booktitle\":\"International Planning Competition Booklet (IPC-8)\",\"keywords\":\"plan generation\",\"year\":\"2014\",\"bibtex\":\"@inproceedings{lipovetzky2014width,\\n  title={Width and Inference Based Planners: SIW, BFS (f), and PROBE},\\n  author={Lipovetzky, Nir and Ramirez, Miquel and Muise, Christian and Geffner, Hector},\\n  url={http://people.eng.unimelb.edu.au/nlipovetzky/papers/ipc8_siw_bfs_probe.pdf},\\n  booktitle={International Planning Competition Booklet (IPC-8)},\\n  keywords = {plan generation},\\n  year={2014}\\n}\\n\\n\",\"author_short\":[\"Lipovetzky, N.\",\"Ramirez, M.\",\"Muise, C.\",\"Geffner, H.\"],\"key\":\"lipovetzky2014width\",\"id\":\"lipovetzky2014width\",\"bibbaseid\":\"lipovetzky-ramirez-muise-geffner-widthandinferencebasedplannerssiwbfsfandprobe-2014\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://people.eng.unimelb.edu.au/nlipovetzky/papers/ipc8_siw_bfs_probe.pdf\"},\"keyword\":[\"plan generation\"],\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":8,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"abstract\":\"Planning with sensing actions under partial observability is a computationally challenging problem that is fundamental to the realization of AI tasks in areas as diverse as robotics, game playing, and diagnostic problem solving. Recent work on generating plans for partially observable domains has advocated for online planning, claiming that offline plans are often too large to generate. Here we push the envelope on this challenging problem, proposing a technique for generating conditional (aka contingent) plans offline. The key to our planner’s success is the reliance on state-of-the-art techniques for fully observable non-deterministic (FOND) planning. In particular, we use an existing compilation for converting a planning problem under partial observability and sensing to a FOND planning problem. With a modified FOND planner in hand, we are able to scale beyond previous techniques for generating conditional plans with solutions that are orders of magnitude smaller than previously possible in some domains.\",\"title\":\"Computing Contingent Plans via Fully Observable Non-Deterministic Planning\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Muise\"],\"firstnames\":[\"Christian\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Belle\"],\"firstnames\":[\"Vaishak\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"McIlraith\"],\"firstnames\":[\"Sheila\",\"A.\"],\"suffixes\":[]}],\"booktitle\":\"The 28th AAAI Conference on Artificial Intelligence\",\"year\":\"2014\",\"keywords\":\"non-deterministic planning, plan generation, partial observability, sensing\",\"url\":\"http://www.haz.ca/papers/muise-aaai-14.pdf\",\"bibtex\":\"@inproceedings{muise-aaai-14,\\n  abstract={Planning with sensing actions under partial observability is\\na computationally challenging problem that is fundamental\\nto the realization of AI tasks in areas as diverse as robotics,\\ngame playing, and diagnostic problem solving. Recent work\\non generating plans for partially observable domains has advocated\\nfor online planning, claiming that offline plans are\\noften too large to generate. Here we push the envelope on\\nthis challenging problem, proposing a technique for generating\\nconditional (aka contingent) plans offline. The key to our\\nplanner’s success is the reliance on state-of-the-art techniques\\nfor fully observable non-deterministic (FOND) planning. In\\nparticular, we use an existing compilation for converting a\\nplanning problem under partial observability and sensing to\\na FOND planning problem. With a modified FOND planner\\nin hand, we are able to scale beyond previous techniques for\\ngenerating conditional plans with solutions that are orders of\\nmagnitude smaller than previously possible in some domains.},\\n  title={Computing Contingent Plans via Fully Observable Non-Deterministic Planning},\\n  author={Muise, Christian and Belle, Vaishak and McIlraith, Sheila A.},\\n  booktitle={The 28th AAAI Conference on Artificial Intelligence},\\n  year={2014},\\n  keywords = {non-deterministic planning, plan generation, partial observability, sensing},\\n  url={http://www.haz.ca/papers/muise-aaai-14.pdf}\\n}\\n\\n\",\"author_short\":[\"Muise, C.\",\"Belle, V.\",\"McIlraith, S. A.\"],\"key\":\"muise-aaai-14\",\"id\":\"muise-aaai-14\",\"bibbaseid\":\"muise-belle-mcilraith-computingcontingentplansviafullyobservablenondeterministicplanning-2014\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.haz.ca/papers/muise-aaai-14.pdf\"},\"keyword\":[\"non-deterministic planning\",\"plan generation\",\"partial observability\",\"sensing\"],\"metadata\":{\"authorlinks\":{\"mcilraith, s\":\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\",\"muise, c\":\"https://www.haz.ca/academic-publications.html\",\"belle, v\":\"https://bibbase.org/show?bib=https://dblp.org/pid/52/570.bib\"}},\"downloads\":65,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"abstract\":\"Recent advances in fully observable non-deterministic (FOND) planning have enabled new techniques for various applications, such as behaviour composition, among others. One key limitation of modern FOND planners is their lack of native support for conditional effects. In this paper we describe an extension to PRP, the current state of the art in FOND planning, that supports the generation of policies for domains with conditional effects and non-determinism. We present core modifications to the PRP planner for this enhanced functionality without sacrificing soundness and completeness. Additionally, we demonstrate the planner’s capabilities on a variety of benchmarks that include actions with both conditional effects and non-deterministic outcomes. The resulting planner opens the door to models of greater expressivity, and does so without affecting PRP’s efficiency.\",\"title\":\"Non-Deterministic Planning With Conditional Effects\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Muise\"],\"firstnames\":[\"Christian\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"McIlraith\"],\"firstnames\":[\"Sheila\",\"A.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Belle\"],\"firstnames\":[\"Vaishak\"],\"suffixes\":[]}],\"booktitle\":\"The 24th International Conference on Automated Planning and Scheduling\",\"year\":\"2014\",\"keywords\":\"non-deterministic planning, plan generation\",\"url\":\"http://www.haz.ca/papers/muise-icaps-14.pdf\",\"bibtex\":\"@inproceedings{muise-icaps-14,\\n  abstract={Recent advances in fully observable non-deterministic\\n(FOND) planning have enabled new techniques for various\\napplications, such as behaviour composition, among others.\\nOne key limitation of modern FOND planners is their lack\\nof native support for conditional effects. In this paper we\\ndescribe an extension to PRP, the current state of the art in\\nFOND planning, that supports the generation of policies for\\ndomains with conditional effects and non-determinism. We\\npresent core modifications to the PRP planner for this enhanced\\nfunctionality without sacrificing soundness and completeness.\\nAdditionally, we demonstrate the planner’s capabilities on a\\nvariety of benchmarks that include actions with\\nboth conditional effects and non-deterministic outcomes. The\\nresulting planner opens the door to models of greater\\nexpressivity, and does so without affecting PRP’s efficiency.},\\n  title={Non-Deterministic Planning With Conditional Effects},\\n  author={Muise, Christian and McIlraith, Sheila A. and Belle, Vaishak},\\n  booktitle={The 24th International Conference on Automated Planning and Scheduling},\\n  year={2014},\\n  keywords = {non-deterministic planning, plan generation},\\n  url={http://www.haz.ca/papers/muise-icaps-14.pdf}\\n}\\n\\n\\n\",\"author_short\":[\"Muise, C.\",\"McIlraith, S. A.\",\"Belle, V.\"],\"key\":\"muise-icaps-14\",\"id\":\"muise-icaps-14\",\"bibbaseid\":\"muise-mcilraith-belle-nondeterministicplanningwithconditionaleffects-2014\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.haz.ca/papers/muise-icaps-14.pdf\"},\"keyword\":[\"non-deterministic planning\",\"plan generation\"],\"metadata\":{\"authorlinks\":{\"mcilraith, s\":\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\",\"muise, c\":\"https://www.haz.ca/academic-publications.html\",\"belle, v\":\"https://bibbase.org/show?bib=https://dblp.org/pid/52/570.bib\"}},\"downloads\":53,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"SAT-based Analysis and Quantification of Information Flow in Programs\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Klebanov\"],\"firstnames\":[\"Vladimir\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Manthey\"],\"firstnames\":[\"Norbert\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Muise\"],\"firstnames\":[\"Christian\"],\"suffixes\":[]}],\"booktitle\":\"10th International Conference on Quantitative Evaluation of SysTems (QEST 2013)\",\"year\":\"2013\",\"pages\":\"177–192\",\"keywords\":\"sat, knowledge compilation\",\"url\":\"http://formal.iti.kit.edu/~klebanov/pubs/qest2013.pdf\",\"bibtex\":\"@inproceedings{klebanovsat,\\n  title={{SAT-based Analysis and Quantification of Information Flow in Programs}},\\n  author={Klebanov, Vladimir and Manthey, Norbert and Muise, Christian},\\n  booktitle={10th International Conference on Quantitative Evaluation of SysTems (QEST 2013)},\\n  year={2013},\\n  pages={177--192},\\n  keywords = {sat, knowledge compilation},\\n  url={http://formal.iti.kit.edu/~klebanov/pubs/qest2013.pdf}\\n}\\n\\n\",\"author_short\":[\"Klebanov, V.\",\"Manthey, N.\",\"Muise, C.\"],\"key\":\"klebanovsat\",\"id\":\"klebanovsat\",\"bibbaseid\":\"klebanov-manthey-muise-satbasedanalysisandquantificationofinformationflowinprograms-2013\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://formal.iti.kit.edu/~klebanov/pubs/qest2013.pdf\"},\"keyword\":[\"sat\",\"knowledge compilation\"],\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":61,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Flexible Execution of Partial Order Plans With Temporal Constraints\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Muise\"],\"firstnames\":[\"Christian\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Beck\"],\"firstnames\":[\"J.\",\"Christopher\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"McIlraith\"],\"firstnames\":[\"Sheila\",\"A.\"],\"suffixes\":[]}],\"booktitle\":\"International Joint Conference On Artificial Intelligence\",\"year\":\"2013\",\"pages\":\"2328–2335\",\"keywords\":\"plan execution,temporal\",\"url\":\"http://ijcai.org/papers13/Papers/IJCAI13-343.pdf\",\"bibtex\":\"@inproceedings{muise-ijcai-13,\\n  title={{Flexible Execution of Partial Order Plans With Temporal Constraints}},\\n  author={Muise, Christian and Beck, J. Christopher and McIlraith, Sheila A.},\\n  booktitle={International Joint Conference On Artificial Intelligence},\\n  year={2013},\\n  pages={2328--2335},\\n  keywords = {plan execution,temporal},\\n  url={http://ijcai.org/papers13/Papers/IJCAI13-343.pdf}\\n}\\n\\n\",\"author_short\":[\"Muise, C.\",\"Beck, J. C.\",\"McIlraith, S. A.\"],\"key\":\"muise-ijcai-13\",\"id\":\"muise-ijcai-13\",\"bibbaseid\":\"muise-beck-mcilraith-flexibleexecutionofpartialorderplanswithtemporalconstraints-2013\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://ijcai.org/papers13/Papers/IJCAI13-343.pdf\"},\"keyword\":[\"plan execution\",\"temporal\"],\"metadata\":{\"authorlinks\":{\"mcilraith, s\":\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\",\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":14,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"abstract\":\"We address the problem of computing a policy for fully ob- servable non-deterministic (FOND) planning problems. By focusing on the relevant aspects of the state of the world, we introduce a series of improvements to the previous state of the art and extend the applicability of our planner, PRP, to work in an online setting. The use of state relevance allows our policy to be exponentiallymore succinct in representing a solution to a FOND problem for some domains. Through the introduction of newtechniques for avoiding deadends and de- termining sufficient validity conditions, PRP has the potential to compute a policy up to several orders of magnitude faster than previous approaches. We also find dramatic improve- ments over the state of the art in online replanning when we treat suitable probabilistic domains as FOND domains.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Muise\"],\"firstnames\":[\"Christian\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"McIlraith\"],\"firstnames\":[\"Sheila\",\"A.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Beck\"],\"firstnames\":[\"J.\",\"Christopher\"],\"suffixes\":[]}],\"booktitle\":\"The 22nd International Conference on Automated Planning and Scheduling\",\"series\":\"The 22nd International Conference on Automated Planning and Scheduling\",\"title\":\"Improved Non-deterministic Planning by Exploiting State Relevance\",\"keywords\":\"plan generation,non-deterministic planning\",\"year\":\"2012\",\"url\":\"http://www.haz.ca/papers/muise-icaps2012-fond.pdf\",\"bibtex\":\"@inproceedings{muise-icaps12-long,\\nabstract = {We address the problem of computing a policy for fully ob- servable non-deterministic (FOND) planning problems. By focusing on the relevant aspects of the state of the world, we introduce a series of improvements to the previous state of the art and extend the applicability of our planner, PRP, to work in an online setting. The use of state relevance allows our policy to be exponentiallymore succinct in representing a solution to a FOND problem for some domains. Through the introduction of newtechniques for avoiding deadends and de- termining sufficient validity conditions, PRP has the potential to compute a policy up to several orders of magnitude faster than previous approaches. We also find dramatic improve- ments over the state of the art in online replanning when we treat suitable probabilistic domains as FOND domains.},\\nauthor = {Muise, Christian and McIlraith, Sheila A. and Beck, J. Christopher},\\nbooktitle = {The 22nd International Conference on Automated Planning and Scheduling},\\nseries = {The 22nd International Conference on Automated Planning and Scheduling},\\ntitle = {{Improved Non-deterministic Planning by Exploiting State Relevance}},\\nkeywords = {plan generation,non-deterministic planning},\\nyear = {2012},\\nurl = {http://www.haz.ca/papers/muise-icaps2012-fond.pdf}\\n}\\n\\n\",\"author_short\":[\"Muise, C.\",\"McIlraith, S. A.\",\"Beck, J. C.\"],\"key\":\"muise-icaps12-long\",\"id\":\"muise-icaps12-long\",\"bibbaseid\":\"muise-mcilraith-beck-improvednondeterministicplanningbyexploitingstaterelevance-2012\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.haz.ca/papers/muise-icaps2012-fond.pdf\"},\"keyword\":[\"plan generation\",\"non-deterministic planning\"],\"metadata\":{\"authorlinks\":{\"mcilraith, s\":\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\",\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":99,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Optimally Relaxing Partial-order Plans With MaxSAT\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Muise\"],\"firstnames\":[\"Christian\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"McIlratih\"],\"firstnames\":[\"Sheila\",\"A.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Beck\"],\"firstnames\":[\"J.\",\"Christopher\"],\"suffixes\":[]}],\"booktitle\":\"22nd International Conference on Automated Planning and Scheduling\",\"url\":\"http://tidel.mie.utoronto.ca/pubs/muise_MaxSATICAPS2012.pdf\",\"keywords\":\"sat,plan optimization\",\"year\":\"2012\",\"bibtex\":\"@inproceedings{muise2012optimally,\\n  title={{Optimally Relaxing Partial-order Plans With MaxSAT}},\\n  author={Muise, Christian and McIlratih, Sheila A. and Beck, J. Christopher},\\n  booktitle={22nd International Conference on Automated Planning and Scheduling},\\n  url={http://tidel.mie.utoronto.ca/pubs/muise_MaxSATICAPS2012.pdf},\\n  keywords = {sat,plan optimization},\\n  year={2012}\\n}\\n\\n\",\"author_short\":[\"Muise, C.\",\"McIlratih, S. A.\",\"Beck, J. C.\"],\"key\":\"muise2012optimally\",\"id\":\"muise2012optimally\",\"bibbaseid\":\"muise-mcilratih-beck-optimallyrelaxingpartialorderplanswithmaxsat-2012\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://tidel.mie.utoronto.ca/pubs/muise_MaxSATICAPS2012.pdf\"},\"keyword\":[\"sat\",\"plan optimization\"],\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":7,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Muise\"],\"firstnames\":[\"Christian\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"McIlraith\"],\"firstnames\":[\"Sheila\",\"A.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Beck\"],\"firstnames\":[\"J.\",\"Christopher\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Hsu\"],\"firstnames\":[\"Eric\"],\"suffixes\":[]}],\"booktitle\":\"Canadian Conference on Artificial Intelligence\",\"title\":\"DSHARP: Fast d-DNNF Compilation with sharpSAT\",\"keywords\":\"sat, knowledge compilation\",\"url\":\"http://tidel.mie.utoronto.ca/pubs/MuiseDSHARP_AI2012.pdf\",\"year\":\"2012\",\"bibtex\":\"@inproceedings{Muise2012,\\nauthor = {Muise, Christian and McIlraith, Sheila A. and Beck, J. Christopher and Hsu, Eric},\\nbooktitle = {Canadian Conference on Artificial Intelligence},\\ntitle = {{DSHARP: Fast d-DNNF Compilation with sharpSAT}},\\nkeywords = {sat, knowledge compilation},\\nurl = {http://tidel.mie.utoronto.ca/pubs/MuiseDSHARP_AI2012.pdf},\\nyear = {2012}\\n}\\n\\n\",\"author_short\":[\"Muise, C.\",\"McIlraith, S. A.\",\"Beck, J. C.\",\"Hsu, E.\"],\"key\":\"Muise2012\",\"id\":\"Muise2012\",\"bibbaseid\":\"muise-mcilraith-beck-hsu-dsharpfastddnnfcompilationwithsharpsat-2012\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://tidel.mie.utoronto.ca/pubs/MuiseDSHARP_AI2012.pdf\"},\"keyword\":[\"sat\",\"knowledge compilation\"],\"metadata\":{\"authorlinks\":{\"mcilraith, s\":\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\",\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":7,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"On supervising agents in situation-determined ConGolog\",\"author\":[{\"propositions\":[],\"lastnames\":[\"De\",\"Giacomo\"],\"firstnames\":[\"Giuseppe\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Lespérance\"],\"firstnames\":[\"Yves\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Muise\"],\"firstnames\":[\"Christian\"],\"suffixes\":[]}],\"booktitle\":\"Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems-Volume 2\",\"pages\":\"1031–1038\",\"year\":\"2012\",\"keywords\":\"sitcalc\",\"organization\":\"International Foundation for Autonomous Agents and Multiagent Systems\",\"url\":\"http://www.cse.yorku.ca/~lesperan/papers/AAMAS12.pdf\",\"bibtex\":\"@inproceedings{de2012supervising,\\n  title={{On supervising agents in situation-determined ConGolog}},\\n  author={De Giacomo, Giuseppe and Lesp{\\\\'e}rance, Yves and Muise, Christian},\\n  booktitle={Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems-Volume 2},\\n  pages={1031--1038},\\n  year={2012},\\n  keywords = {sitcalc},\\n  organization={International Foundation for Autonomous Agents and Multiagent Systems},\\n  url={http://www.cse.yorku.ca/~lesperan/papers/AAMAS12.pdf}\\n}\\n\\n\",\"author_short\":[\"De Giacomo, G.\",\"Lespérance, Y.\",\"Muise, C.\"],\"key\":\"de2012supervising\",\"id\":\"de2012supervising\",\"bibbaseid\":\"degiacomo-lesprance-muise-onsupervisingagentsinsituationdeterminedcongolog-2012\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.cse.yorku.ca/~lesperan/papers/AAMAS12.pdf\"},\"keyword\":[\"sitcalc\"],\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"abstract\":\"Partial-order plans (POPs) have the capacity to compactly represent numerous distinct plan lin- earizations and as a consequence are inherently ro- bust. We exploit this robustness to do effective ex- ecutionmonitoring. We characterize the conditions underwhich a POP remains viable as the regression of the goal through the structure of a POP.We then develop a method for POP execution monitoring via a structured policy, expressed as an ordered al- gebraic decision diagram. The policy encompasses both state evaluation and action selection, enabling an agent to seamlessly switch between POP lin- earizations to accommodate unexpected changes during execution. We demonstrate the effective- ness of our approach by comparing it empirically and analytically to a standard technique for execu- tion monitoring of sequential plans. On standard benchmark planning domains, our approach is 2 to 17 times faster and up to 2.5 times more robust than comparable monitoring of a sequential plan. On POPs that have few ordering constraints among actions, our approach is significantly more robust, with the ability to continue executing in up to an exponential number of additional states.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Muise\"],\"firstnames\":[\"Christian\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"McIlraith\"],\"firstnames\":[\"Sheila\",\"A.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Beck\"],\"firstnames\":[\"J.\",\"Christopher\"],\"suffixes\":[]}],\"booktitle\":\"International Joint Conference On Artificial Intelligence\",\"series\":\"International Joint Conference On Artificial Intelligence\",\"title\":\"Monitoring the Execution of Partial-Order Plans via Regression\",\"year\":\"2011\",\"keywords\":\"plan execution\",\"url\":\"http://ijcai.org/papers11/Papers/IJCAI11-330.pdf\",\"bibtex\":\"@inproceedings{muise-ijcai11,\\nabstract = {Partial-order plans (POPs) have the capacity to compactly represent numerous distinct plan lin- earizations and as a consequence are inherently ro- bust. We exploit this robustness to do effective ex- ecutionmonitoring. We characterize the conditions underwhich a POP remains viable as the regression of the goal through the structure of a POP.We then develop a method for POP execution monitoring via a structured policy, expressed as an ordered al- gebraic decision diagram. The policy encompasses both state evaluation and action selection, enabling an agent to seamlessly switch between POP lin- earizations to accommodate unexpected changes during execution. We demonstrate the effective- ness of our approach by comparing it empirically and analytically to a standard technique for execu- tion monitoring of sequential plans. On standard benchmark planning domains, our approach is 2 to 17 times faster and up to 2.5 times more robust than comparable monitoring of a sequential plan. On POPs that have few ordering constraints among actions, our approach is significantly more robust, with the ability to continue executing in up to an exponential number of additional states.},\\nauthor = {Muise, Christian and McIlraith, Sheila A. and Beck, J. Christopher},\\nbooktitle = {International Joint Conference On Artificial Intelligence},\\nseries = {International Joint Conference On Artificial Intelligence},\\ntitle = {{Monitoring the Execution of Partial-Order Plans via Regression}},\\nyear = {2011},\\nkeywords = {plan execution},\\nurl = {http://ijcai.org/papers11/Papers/IJCAI11-330.pdf}\\n}\\n\\n\",\"author_short\":[\"Muise, C.\",\"McIlraith, S. A.\",\"Beck, J. C.\"],\"key\":\"muise-ijcai11\",\"id\":\"muise-ijcai11\",\"bibbaseid\":\"muise-mcilraith-beck-monitoringtheexecutionofpartialorderplansviaregression-2011\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://ijcai.org/papers11/Papers/IJCAI11-330.pdf\"},\"keyword\":[\"plan execution\"],\"metadata\":{\"authorlinks\":{\"mcilraith, s\":\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\",\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":14,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Optimization of Partial-Order Plans via MaxSAT\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Muise\"],\"firstnames\":[\"Christian\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"McIlratih\"],\"firstnames\":[\"Sheila\",\"A.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Beck\"],\"firstnames\":[\"J.\",\"Christopher\"],\"suffixes\":[]}],\"booktitle\":\"Workshop on Constraint Satisfaction Techniques for Planning and Scheduling Problems (COPLAS'11)\",\"year\":\"2011\",\"keywords\":\"sat,plan optimization\",\"url\":\"http://www.haz.ca/papers/coplas-muise-11.pdf\",\"bibtex\":\"@inproceedings{muise2011optimally,\\n  title={{Optimization of Partial-Order Plans via MaxSAT}},\\n  author={Muise, Christian and McIlratih, Sheila A. and Beck, J. Christopher},\\n  booktitle={Workshop on Constraint Satisfaction Techniques for Planning and Scheduling Problems (COPLAS'11)},\\n  year={2011},\\n  keywords = {sat,plan optimization},\\n  url={http://www.haz.ca/papers/coplas-muise-11.pdf}\\n}\\n\\n\",\"author_short\":[\"Muise, C.\",\"McIlratih, S. A.\",\"Beck, J. C.\"],\"key\":\"muise2011optimally\",\"id\":\"muise2011optimally\",\"bibbaseid\":\"muise-mcilratih-beck-optimizationofpartialorderplansviamaxsat-2011\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.haz.ca/papers/coplas-muise-11.pdf\"},\"keyword\":[\"sat\",\"plan optimization\"],\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"abstract\":\"We investigate agent supervision, a form of customization, which constrains the actions of an agent so as to enforce certain desired behavioral specifications. This is done in a setting based on the Situation Calculus and a variant of the ConGolog programming language which allows for nondeterminism, but requires the remainder of a program after the execution of an action to be determined by the resulting situation. Such programs can be fully characterized by the set of action sequences that they generate. The main results are a characterization of the maximally permissive supervisor that minimally constrains the agent so as to enforce the desired behavioral constraints when some agent actions are uncontrollable, and a sound and complete technique to execute the agent as constrained by such a supervisor.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"De Giacomo\"],\"firstnames\":[\"Giuseppe\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Lesperance\"],\"firstnames\":[\"Yves\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Muise\"],\"firstnames\":[\"Christian\"],\"suffixes\":[]}],\"booktitle\":\"Nonmonotonic Reasoning, Action and Change\",\"title\":\"Agent Supervision in Situation-Determined ConGolog\",\"url\":\"http://ijcai-11.iiia.csic.es/files/proceedings/W4- NRAC11-Proceedings.pdf\\\\#page=27\",\"keywords\":\"sitcalc\",\"year\":\"2011\",\"bibtex\":\"@inproceedings{DeGiacomo2011,\\nabstract = {We investigate agent supervision, a form of customization, which constrains the actions of an agent so as to enforce certain desired behavioral specifications. This is done in a setting based on the Situation Calculus and a variant of the ConGolog programming language which allows for nondeterminism, but requires the remainder of a program after the execution of an action to be determined by the resulting situation. Such programs can be fully characterized by the set of action sequences that they generate. The main results are a characterization of the maximally permissive supervisor that minimally constrains the agent so as to enforce the desired behavioral constraints when some agent actions are uncontrollable, and a sound and complete technique to execute the agent as constrained by such a supervisor.},\\nauthor = {{De Giacomo}, Giuseppe and Lesperance, Yves and Muise, Christian},\\nbooktitle = {Nonmonotonic Reasoning, Action and Change},\\ntitle = {{Agent Supervision in Situation-Determined ConGolog}},\\nurl = {http://ijcai-11.iiia.csic.es/files/proceedings/W4- NRAC11-Proceedings.pdf\\\\#page=27},\\nkeywords = {sitcalc},\\nyear = {2011}\\n}\\n\\n\",\"author_short\":[\"De Giacomo, G.\",\"Lesperance, Y.\",\"Muise, C.\"],\"key\":\"DeGiacomo2011\",\"id\":\"DeGiacomo2011\",\"bibbaseid\":\"degiacomo-lesperance-muise-agentsupervisioninsituationdeterminedcongolog-2011\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://ijcai-11.iiia.csic.es/files/proceedings/W4- NRAC11-Proceedings.pdf\\\\#page=27\"},\"keyword\":[\"sitcalc\"],\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"Atlanta, GA, USA\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Muise\"],\"firstnames\":[\"Christian\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"McIlraith\"],\"firstnames\":[\"Sheila\",\"A.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Beck\"],\"firstnames\":[\"J.\",\"Christopher\"],\"suffixes\":[]}],\"booktitle\":\"Workshop on Abstraction, Reformulation, and Approximation (AAAI-10)\",\"title\":\"Fast d-DNNF Compilation with sharpSAT\",\"url\":\"http://www.haz.ca/papers/wara-muise-10.pdf\",\"keywords\":\"sat, knowledge compilation\",\"year\":\"2010\",\"bibtex\":\"@inproceedings{mui-etal-wara10,\\naddress = {Atlanta, GA, USA},\\nauthor = {Muise, Christian and McIlraith, Sheila A. and Beck, J. Christopher},\\nbooktitle = {Workshop on Abstraction, Reformulation, and Approximation (AAAI-10)},\\ntitle = {{Fast d-DNNF Compilation with sharpSAT}},\\nurl = {http://www.haz.ca/papers/wara-muise-10.pdf},\\nkeywords = {sat, knowledge compilation},\\nyear = {2010}\\n}\\n\\n\",\"author_short\":[\"Muise, C.\",\"McIlraith, S. A.\",\"Beck, J. C.\"],\"key\":\"mui-etal-wara10\",\"id\":\"mui-etal-wara10\",\"bibbaseid\":\"muise-mcilraith-beck-fastddnnfcompilationwithsharpsat-2010\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.haz.ca/papers/wara-muise-10.pdf\"},\"keyword\":[\"sat\",\"knowledge compilation\"],\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"Thessaloniki, Greece\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Muise\"],\"firstnames\":[\"Christian\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"McIlraith\"],\"firstnames\":[\"Sheila\",\"A.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Baier\"],\"firstnames\":[\"Jorge\",\"A.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Reimer\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]}],\"booktitle\":\"19th International Conference on Automated Planning and Scheduling\",\"title\":\"Exploiting N-gram Analysis to Predict Operator Sequences\",\"url\":\"http://www.haz.ca/papers/mui-etal-icaps09.pdf\",\"year\":\"2009\",\"keywords\":\"learning\",\"bibtex\":\"@inproceedings{Muise2009,\\naddress = {Thessaloniki, Greece},\\nauthor = {Muise, Christian and McIlraith, Sheila A. and Baier, Jorge A. and Reimer, Michael},\\nbooktitle = {19th International Conference on Automated Planning and Scheduling},\\ntitle = {{Exploiting N-gram Analysis to Predict Operator Sequences}},\\nurl = {http://www.haz.ca/papers/mui-etal-icaps09.pdf},\\nyear = {2009},\\nkeywords = {learning},\\n}\\n\\n\",\"author_short\":[\"Muise, C.\",\"McIlraith, S. A.\",\"Baier, J. A.\",\"Reimer, M.\"],\"key\":\"Muise2009\",\"id\":\"Muise2009\",\"bibbaseid\":\"muise-mcilraith-baier-reimer-exploitingngramanalysistopredictoperatorsequences-2009\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.haz.ca/papers/mui-etal-icaps09.pdf\"},\"keyword\":[\"learning\"],\"metadata\":{\"authorlinks\":{\"mcilraith, s\":\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\",\"muise, c\":\"https://www.haz.ca/academic-publications.html\",\"baier, j\":\"http://www.cs.toronto.edu/~bgmomb/research.html\"}},\"downloads\":14,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"Sydney, Australia\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Hsu\"],\"firstnames\":[\"Eric\",\"I\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Muise\"],\"firstnames\":[\"Christian\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Beck\"],\"firstnames\":[\"J.\",\"Christopher\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"McIlraith\"],\"firstnames\":[\"Sheila\",\"A.\"],\"suffixes\":[]}],\"booktitle\":\"Principles and Practice of Constraint Programming, 14th International Conference\",\"pages\":\"613–617\",\"keywords\":\"sat\",\"title\":\"Probabilistically Estimating Backbones and Variable Bias: Experimental Overview\",\"url\":\"http://www.haz.ca/papers/hsu-cp2008.pdf\",\"year\":\"2008\",\"bibtex\":\"@inproceedings{DBLP:conf/cp/HsuMBM08,\\naddress = {Sydney, Australia},\\nauthor = {Hsu, Eric I and Muise, Christian and Beck, J. Christopher and McIlraith, Sheila A.},\\nbooktitle = {Principles and Practice of Constraint Programming, 14th International Conference},\\npages = {613--617},\\nkeywords = {sat},\\ntitle = {{Probabilistically Estimating Backbones and Variable Bias: Experimental Overview}},\\nurl = {http://www.haz.ca/papers/hsu-cp2008.pdf},\\nyear = {2008}\\n}\\n\\n\",\"author_short\":[\"Hsu, E. I\",\"Muise, C.\",\"Beck, J. C.\",\"McIlraith, S. A.\"],\"key\":\"DBLP:conf/cp/HsuMBM08\",\"id\":\"DBLP:conf/cp/HsuMBM08\",\"bibbaseid\":\"hsu-muise-beck-mcilraith-probabilisticallyestimatingbackbonesandvariablebiasexperimentaloverview-2008\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.haz.ca/papers/hsu-cp2008.pdf\"},\"keyword\":[\"sat\"],\"metadata\":{\"authorlinks\":{\"mcilraith, s\":\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\",\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":4,\"html\":\"\"},{\"bibtype\":\"mastersthesis\",\"type\":\"mastersthesis\",\"author\":[{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]}],\"title\":\"Exploiting Modern #SAT-Solving Techniques to Generate Implicants\",\"school\":\"University of Toronto\",\"year\":\"2009\",\"keywords\":\"thesis\",\"url\":\"http://www.haz.ca/papers/Muise-Masters-Thesis.pdf\",\"abstract\":\"An implicant of a propositional boolean formula is a conjunction of propositions that entails a the formula. Implicants play an important role for many tasks in AI such as knowledge compilation, circuit minimization, and diagnosis. There are many classes of implicants that are of interest including prime, irredundant, and orthogonal implicants. In general, implicants are hard to compute because the size of both the intermediate and/or final results can be exponentially large. In this work we focus on how advances in modern #SAT solvers can be exploited to effectively compute a complete set of orthogonal implicants. We develop an orthogonal implicant compiler and demonstrate its potential through experimental evaluation. Preliminary results indicate that this approach is effective at generating the orthogonal implicants of a propositional theory represented in Conjunctive Normal Form.\",\"bibtex\":\"@mastersthesis{muise-mastersthesis,\\n  author       = {Christian Muise},\\n  title        = {Exploiting Modern \\\\#SAT-Solving Techniques to Generate Implicants},\\n  school       = {University of Toronto},\\n  year         = 2009,\\n  keywords     = {thesis},\\n  url          = {http://www.haz.ca/papers/Muise-Masters-Thesis.pdf},\\n  abstract     = {An implicant of a propositional boolean formula is a conjunction of propositions that entails a the formula. Implicants play an important role for many tasks in AI such as knowledge compilation, circuit minimization, and diagnosis. There are many classes of implicants that are of interest including prime, irredundant, and orthogonal implicants. In general, implicants are hard to compute because the size of both the intermediate and/or final results can be exponentially large. In this work we focus on how advances in modern #SAT solvers can be exploited to effectively compute a complete set of orthogonal implicants. We develop an orthogonal implicant compiler and demonstrate its potential through experimental evaluation. Preliminary results indicate that this approach is effective at generating the orthogonal implicants of a propositional theory represented in Conjunctive Normal Form.}\\n}\\n\\n\",\"author_short\":[\"Muise, C.\"],\"key\":\"muise-mastersthesis\",\"id\":\"muise-mastersthesis\",\"bibbaseid\":\"muise-exploitingmodernsatsolvingtechniquestogenerateimplicants-2009\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.haz.ca/papers/Muise-Masters-Thesis.pdf\"},\"keyword\":[\"thesis\"],\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":3,\"html\":\"\"},{\"bibtype\":\"techreport\",\"type\":\"techreport\",\"title\":\"Plan Dispatchability: A Survey\",\"author\":[{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]}],\"year\":\"2011\",\"institution\":\"University of Toronto, Depth Examination Report\",\"keywords\":\"temporal\",\"url\":\"http://www.haz.ca/papers/muise-depth.pdf\",\"url_slides\":\"http://www.haz.ca/papers/muise-depth_slides.pdf\",\"bibtex\":\"@techreport{muise-depth,\\n  title={Plan Dispatchability: A Survey},\\n  author={Christian Muise},\\n  year={2011},\\n  institution={University of Toronto, Depth Examination Report},\\n  keywords={temporal},\\n  url={http://www.haz.ca/papers/muise-depth.pdf},\\n  url_slides={http://www.haz.ca/papers/muise-depth_slides.pdf}\\n}\\n\\n\",\"author_short\":[\"Muise, C.\"],\"key\":\"muise-depth\",\"id\":\"muise-depth\",\"bibbaseid\":\"muise-plandispatchabilityasurvey-2011\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.haz.ca/papers/muise-depth.pdf\",\" slides\":\"http://www.haz.ca/papers/muise-depth_slides.pdf\"},\"keyword\":[\"temporal\"],\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":3,\"html\":\"\"},{\"bibtype\":\"phdthesis\",\"type\":\"phdthesis\",\"author\":[{\"firstnames\":[\"Christian\"],\"propositions\":[],\"lastnames\":[\"Muise\"],\"suffixes\":[]}],\"title\":\"Exploiting Relevance to Improve Robustness and Flexibility in Plan Generation and Execution\",\"school\":\"University of Toronto\",\"year\":\"2014\",\"keywords\":\"thesis\",\"url\":\"http://www.haz.ca/papers/Muise-PhD-Thesis.pdf\",\"abstract\":\"Automated plan generation and execution is an essential component of most autonomous agents. An agent’s model of the world is often incomplete or incorrect, and its environment is typically noisy. To account for potential discrepancies between the agent’s model of the world and the true state of the world, the planning techniques and representations used should enable flexible and robust agent behaviour. The agent should react swiftly when unexpected changes occur to assess the impact of the discrepancy and to accommodate as necessary. In particular, the agent should avoid unnecessary replanning and recognize changes that are irrelevant for its plan to achieve the goal. In this dissertation we address various aspects of the planning process including (1) how to synthesize a plan, (2) what a plan should constitute and how we should represent one, and (3) how to effectively execute a plan. We enable robust and flexible agent behaviour by exploiting the notion of relevance in each of the key planning areas. Intuitively, relevance characterizes what is important to consider as a sufficient condition for some property to hold. We apply relevance to the key areas of automated planning to achieve the following contributions: (1) increased flexibility of partial-order plans, (2) improved robustness of partial-order plan execution, (3) robust execution of temporally constrained plans, and (4) improved efficiency of plan generation with non-deterministic action effects. To increase the flexibility of partial-order plans, we introduce an effective method for generating optimally relaxed partial-order plans. For the execution of a partial-order plan, we leverage regression to generalize an input plan, resulting in an execution monitoring framework that is far more robust than previous approaches. We incorporate the expressive power of temporal constraints and provide a means for monitoring the execution of a temporally constrained plan, building on our approach for executing a partial-order plan. Finally, we introduce a suite of techniques that leverage relevance to produce a state-of-the-art planner for domains with non-deterministic action effects. For each of these four areas, we investigate the theoretical properties surrounding our methods and empirically demonstrate their feasibility in comparison to the previous state of the art.\",\"bibtex\":\"@phdthesis{muise-phdthesis,\\n  author       = {Christian Muise},\\n  title        = {Exploiting Relevance to Improve Robustness and Flexibility in Plan Generation and Execution},\\n  school       = {University of Toronto},\\n  year         = 2014,\\n  keywords     = {thesis},\\n  url          = {http://www.haz.ca/papers/Muise-PhD-Thesis.pdf},\\n  abstract     = {Automated plan generation and execution is an essential component of most autonomous agents. An agent’s model of the world is often incomplete or incorrect, and its environment is typically noisy. To account for potential discrepancies between the agent’s model of the world and the true state of the world, the planning techniques and representations used should enable flexible and robust agent behaviour. The agent should react swiftly when unexpected changes occur to assess the impact of the discrepancy and to accommodate as necessary. In particular, the agent should avoid unnecessary replanning and recognize changes that are irrelevant for its plan to achieve the goal.\\n\\nIn this dissertation we address various aspects of the planning process including (1) how to synthesize a plan, (2) what a plan should constitute and how we should represent one, and (3) how to effectively execute a plan. We enable robust and flexible agent behaviour by exploiting the notion of relevance in each of the key planning areas. Intuitively, relevance characterizes what is important to consider as a sufficient condition for some property to hold. We apply relevance to the key areas of automated planning to achieve the following contributions: (1) increased flexibility of partial-order plans, (2) improved robustness of partial-order plan execution, (3) robust execution of temporally constrained plans, and (4) improved efficiency of plan generation with non-deterministic action effects.\\n\\nTo increase the flexibility of partial-order plans, we introduce an effective method for generating optimally relaxed partial-order plans. For the execution of a partial-order plan, we leverage regression to generalize an input plan, resulting in an execution monitoring framework that is far more robust than previous approaches. We incorporate the expressive power of temporal constraints and provide a means for monitoring the execution of a temporally constrained plan, building on our approach for executing a partial-order plan. Finally, we introduce a suite of techniques that leverage relevance to produce a state-of-the-art planner for domains with non-deterministic action effects. For each of these four areas, we investigate the theoretical properties surrounding our methods and empirically demonstrate their feasibility in comparison to the previous state of the art.}\\n}\\n\",\"author_short\":[\"Muise, C.\"],\"key\":\"muise-phdthesis\",\"id\":\"muise-phdthesis\",\"bibbaseid\":\"muise-exploitingrelevancetoimproverobustnessandflexibilityinplangenerationandexecution-2014\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.haz.ca/papers/Muise-PhD-Thesis.pdf\"},\"keyword\":[\"thesis\"],\"metadata\":{\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\"downloads\":29,\"html\":\"\"}],\n      metadata: {\"owner\":\"muise, c\",\"authorlinks\":{\"muise, c\":\"https://www.haz.ca/academic-publications.html\"}},\n      ownerID: \"f4LpZ8LcDrvDyGuip\"\n    };\n  </script>\n\n  <script type=\"text/javascript\">\n  var site$;\n  if (typeof $ != 'undefined') {\n    console.log('existing jquery: storing and then restoring');\n    site$ = $;\n    $ = null;\n  }\n  </script>\n\n  <script src=\"https://ajax.googleapis.com/ajax/libs/jquery/2.2.4/jquery.min.js\">\n  </script>\n  <script type=\"text/javascript\">\n  if (site$) {\n    console.log('existing jquery: avoiding conflict and restoring');\n    bb$ = $.noConflict(true);\n    $ = site$;\n  } else {\n    bb$ = $;\n  }\n  </script>\n\n  <script src=\"//bibbase.org/js/bibbase.min.js\"\n          type=\"text/javascript\">\n  </script>\n\n  <script type=\"text/javascript\"\n          src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML\">\n  </script>\n  <script type=\"text/x-mathjax-config\">\n    MathJax.Hub.Config({tex2jax: {inlineMath: [['$','$'], ['\\\\(','\\\\)']]},\n                        skipTags: [\"body\"],\n                        processClass: \"bibbase_paper\"});\n  </script>\n\n  <style>\n  @media print {\n    .dontprint {\n        display: none !important;\n    }\n\n  .bibbase_group {\n    background-color: #E0E0E0;\n    font-style: italic;\n    font-size: larger;\n    text-shadow: 0px 0px 3px #FFFFFF;\n    border-radius: 4px 4px 4px 4px;\n    -moz-border-radius: 4px 4px 4px 4px;\n    -webkit-border-radius: 4px;\n    padding: 5px 40px 5px;\n    margin-top: 10px;\n    margin-bottom: 5px;\n    cursor: pointer;\n  }\n\n  .bibbase_paper {\n    margin-bottom: 5px;\n  }\n\n  }\n  </style>\n\n  \n  <div style=\"float: right; margin-right: 10px; margin-top: 10px; text-align: right; font-size: 12px\">\n    generated by\n    <a href=\"//bibbase.org\"\n       onclick=\"trackOutboundLink('bibbase', 'logo clicked', window.location.href, '//bibbase.org'); return false;\">\n      <img src=\"//bibbase.org/img/logo.svg\"\n           style=\"vertical-align: middle; margin-left: 3px; height: 16px;\"/\n           alt=\"bibbase.org\"\n           title=\"BibBase – The easiest way to maintain your publications page.\"\n        ></a>\n    \n  </div>\n  \n\n  <div id=\"bibbase_header\" class=\"dontprint\" style=\"\">\n\n    <ul class=\"nav nav-pills\" style=\"margin: 0px;\">\n\n      <li class=\"dropdown\" id=\"groupby_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\">\n          Group by\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li class=\"groupby year\"><a onclick=\"groupby('year')\">\n            Year</a>\n        </li>\n        <li class=\"groupby author\"><a onclick=\"groupby('author_short')\">\n            Author</a>\n        </li>\n        <li class=\"groupby type\"><a onclick=\"groupby('type')\">\n            Type</a>\n        </li>\n        <li class=\"groupby keyword\"><a onclick=\"groupby('keyword')\">\n            Keyword</a>\n        </li>\n        <li class=\"groupby downloads\"><a onclick=\"groupby('downloads')\">\n            Downloads</a>\n        </li>\n      </ul>\n      </li>\n\n\n      <li class=\"dropdown\" id=\"menu_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\" alt=\"controls\">\n          <i class=\"fa fa-reorder\" alt=\"controls\"></i>\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li>\n          <a onclick=\"toggleAll()\">\n            <i class=\"fa fa-chevron-down fa-fw\"></i> Expand/Collapse All\n          </a>\n        </li>\n        <li>\n          <a download=\"publications.bib\" href=\"data:text/bibtex;base64,@incollection{ding-icapsdemo-2023,
  title     = {Planning as a Service},
  author    = {Yi Ding and Cam Cunningham and Christian Muise and Nir Lipovetzky},
  booktitle = {International Conference on Automated Planning and Scheduling: System Demonstrations},
  year      = {2023},
  url_paper = {https://icaps23.icaps-conference.org/demos/papers/8837_paper.pdf},
  abstract  = {Planning as a service (PaaS) provides an extendable API to deploy planners online in local or cloud servers. The service provides a queue manager to control a set of workers, which can easily be extended with one of several planners available in PLANUTILS. PaaS is designed to overcome the limitations of the existing online solver.planning.domains interface and widen the adoption of planning technology in education, research, and industry.}
}



@incollection{devenezia-icapsdemo-2023,
  title     = {Plan4Dial: A Dialogue Planning Framework},
  author    = {Rebecca De Venezia and Christian Muise},
  booktitle = {International Conference on Automated Planning and Scheduling: System Demonstrations},
  year      = {2023},
  url_paper = {https://icaps23.icaps-conference.org/demos/papers/8139_paper.pdf},
  abstract  = {Dialogue agents have exploded in importance in recent years as businesses increasingly use chat-bots to serve their customer base. However, many of these dialogue systems rely on black-box language models that cannot be verified for predictability, making them a liability. One proposed solution is dialogue planning, which uses planning to generate a complete dialogue tree and allows for the verification of the agent’s actions. Despite the plethora of existing research in this space, there is no open and readily available modern framework for dialogue planning development. We propose Plan4Dial, an open-source system for creating dialogue planning chat-bots. Plan4Dial allows developers to declare complex chat-bots with ease by writing an intuitive YAML specification which the system converts to raw PDDL. Plan4Dial then calls a state-of-the-art planner to generate a dialogue tree which we execute with an extension of IBM’s dialogue plan executor, Hovor. We also created WIDGET, an embeddable web user interface for users to chat with their agents. Our work allows for the simple creation of complex but verifiable goal-oriented dialogue agents using planning technology.}
}



@incollection{morgan-cai-2023,
  title     = {Learning to Recognize Reachable States from Visual Domains},
  author    = {Ella Morgan and Christian Muise},
  booktitle = {Canadian Conference on Artificial Intelligence},
  year      = {2023},
  url_paper = {https://caiac.pubpub.org/pub/0ggeub8s/release/1},
  abstract  = {While planning models are symbolic and precise, the real world is noisy and unstructured. This work aims to bridge the gap between noise and structure by aligning visualizations of planning states to the underlying state space structure. Further, we do so in the presence of noise and augmentations that simulates a commonly overlooked property of real environments: several variations of semantically equivalent states. First, we create a dataset that visualizes states for several common planning domains; each state is generated in a way that introduces variability or noise. E.g., objects changing in location or appearance in a manner that preserves semantic meaning. First we train a contrastive learning model to predict the underlying states from the images. We then evaluate how we can align the predictions of a given sequence of visualized states with the problem’s reachable state space, taking advantage of the known structure to improve predictions. We compare two methods for doing so: a greedy approach and Viterbi’s algorithm, a well-established algorithm for observation decoding given a hidden Markov model. The results demonstrate that these alignment methods can correct errors in the model and significantly improve predictive accuracy.}
}



@incollection{armstrong-intex-2023,
  title     = {The Generalizability of FOND Solutions in Uncertain Environments},
  author    = {Victoria Armstrong and Christian Muise},
  booktitle = {Workshop on Integrated Acting, Planning and Execution},
  year      = {2023},
  url_paper = {https://mulab.ai/papers/2023-intex-armstrong.pdf},
  abstract  = {Logical regression has proven to be a powerful mechanism for computing compact solutions to non-deterministic planning problems. However, the impact on the generality of the generated solutions has thus far been largely unstudied. We analyze the compact solutions produced by a leading FOND planner, PRP, and develop a logical encoding that represents all possible states that the policy can handle. Through the use of a #-SAT solver, we count the number of models that satisfy the logical encoding (corresponding precisely to the states the policy is able to handle). We analyze the solution representation on seven standard FOND benchmarks and compare the generality of these policies to the reachable state space of the policy applied to the problem’s initial state. Our work can be seen as a generalization of similar studies for deterministic planning domains and clearly demonstrates the broad generalizability of these compact representations.}
}



@incollection{petrov-hsdip-2023,
  title     = {From State Spaces to Semigroups: Leveraging Algebraic Formalism for Automated Planning},
  author    = {Alice Petrov and Christian Muise},
  booktitle = {Workshop on Heuristics and Search for Domain-independent Planning},
  year      = {2023},
  url_paper = {https://openreview.net/pdf?id=ocTyIiCroh},
  abstract  = {This paper introduces an algebraic formalism linking transformation semigroups and the state transition systems induced by classical planning problems. We investigate some basic planning problems with interesting properties and establish fundamental characteristics of the corresponding semigroups, such as their ideals and Green’s relations. Furthermore, we leverage semigroup theory to propose new approaches to existing concepts in automated planning, including the identification of landmark actions and the study of dead ends. We demonstrate that algebraic results can be applied to facilitate an understanding of a planning problem’s state space and explore its solutions, thus verifying the relevance and effectiveness of such formal modeling.}
}



@incollection{christen-ecai-2023,
  title     = {PARIS: Planning Algorithms for Reconfiguring Independent Sets},
  author    = {Remo Christen and Salome Eriksson and Michael Katz and Christian Muise and Alice Petrov and Florian Pommerening and Jendrik Seipp and Silvan Sievers and David Speck},
  booktitle = {26th European Conference on Artificial Intelligence},
  year      = {2023},
  abstract  = {Combinatorial reconfiguration studies how one solution of a combinatorial problem can be transformed into another. The transformation can only make small local changes and may not leave the solution space. An important example is the independent set reconfiguration (ISR) problem, where an independent set of a graph (a subset of its vertices without edges between them) has to be transformed into another one by a sequence of modifications that remove a vertex or add another that is not adjacent to any vertex in the set. The 1st Combinatorial Reconfiguration Challenge (CoRe Challenge 2022) was a competition focused on the ISR problem. The PARIS team participated with two solvers that model the ISR problem as a planning problem and employ different planning techniques for solving it. The solvers successfully competed in the challenge and were awarded 4 first, 3 second, and 3 third places across 9 tracks. In this work, we show how to model ISR problems as planning tasks and describe the planning techniques used by these solvers. For a fair comparison to competing ISR approaches, we re-run the entire competition under equal computational conditions. Besides showcasing the success of planning technology, we hope that this work will create a cross-fertilization of the two research fields.},
  url_paper = {https://ebooks.iospress.nl/doi/10.3233/FAIA230303}
}



@incollection{petrov-spark-2023,
  title     = {Planning for Proofs},
  author    = {Alice Petrov and Christian Muise},
  booktitle = {Scheduling and Planning Applications woRKshop},
  year      = {2023},
  abstract  = {This paper explores the application of automated planning to automated theorem proving, which is a branch of automated reasoning concerned with the development of algorithms and computer programs to construct mathematical proofs. In particular, we investigate the use of planning to construct elementary proofs in abstract algebra, which provides a rigorous and axiomatic framework for studying algebraic structures such as groups, rings, fields, and modules. We implement basic implications, equalities, and rules in both deterministic and non-deterministic domains to model commutative rings and deduce elementary results about them. The success of this initial implementation suggests that the well-established techniques seen in automated planning are applicable to the relatively newer field of automated theorem proving. Likewise, automated theorem proving provides a new, challenging domain for automated planning.}
}



@incollection{klassen-haxp-2023,
  title     = {Towards Human-Aware AI via Planning with Epistemic Preferences},
  author    = {Toryn Q. Klassen and Christian Muise and Sheila A. McIlraith},
  booktitle = {International Workshop on Human-Aware and Explainable Planning},
  year      = {2023},
  url_paper = {https://openreview.net/pdf?id=nkn1rcI6wd},
  abstract  = {Within the field of automated planning, two areas of study are planning with preferences, and epistemic planning. Planning with preferences involves generating plans that optimize for properties of the plan instead of, or in addition to, trying to reach a fixed goal. Epistemic planning allows for planning over the knowledge or belief states of one or more agents for the purpose of achieving epistemic goals (where agents have particular states of knowledge or belief). In this paper we motivate and explore the task of planning with epistemic preferences, proposing a method by which existing automated planning techniques can be combined for this purpose. Epistemic preferences may better allow for representing what humans want, and have benefits for AI safety.}
}



@incollection{klassen-kr-2023,
  title     = {Planning with Epistemic Preferences},
  author    = {Toryn Q. Klassen and Christian Muise and Sheila A. McIlraith},
  booktitle = {International Conference on Principles of Knowledge Representation and Reasoning},
  pages     = {752--756},
  year      = {2023},
  url_paper = {https://proceedings.kr.org/2023/76/kr2023-0076-klassen-et-al.pdf},
  abstract  = {Within the field of automated planning, two areas of study are planning with preferences and epistemic planning. Planning with preferences involves generating plans that optimize for properties of the plan instead of, or in addition to, trying to reach a fixed goal. Epistemic planning allows for planning over the knowledge or belief states of one or more agents for the purpose of achieving epistemic goals (where agents have particular states of knowledge or belief). In this paper we motivate and explore the task of planning with epistemic preferences, proposing a method by which existing automated planning techniques can be combined for this purpose.}
}



@incollection{chakraborti-arxiv-2023,
  title     = {TOBY: A Tool for Exploring Data in Academic Survey Papers},
  author    = {Tathagata Chakraborti and Jungkoo Kang and Christian Muise and Sarath Sreedharan and Michael Walker and Daniel Szafir and Tom Williams},
  booktitle = {arXiv},
  year      = {2023},
  url_paper = {https://arxiv.org/abs/2306.10051},
  abstract  = {This paper describes TOBY, a visualization tool that helps a user explore the contents of an academic survey paper. The visualization consists of four components: a hierarchical view of taxonomic data in the survey, a document similarity view in the space of taxonomic classes, a network view of citations, and a new paper recommendation tool. In this paper, we will discuss these features in the context of three separate deployments of the tool.}
}



@article{DBLP:journals/jair/AsaiKFM22,
  author    = {Masataro Asai and
               Hiroshi Kajino and
               Alex Fukunaga and
               Christian Muise},
  title     = {Classical Planning in Deep Latent Space},
  journal   = {Journal of Artificial Intelligence Research},
  volume    = {74},
  pages     = {1599--1686},
  year      = {2022},
  doi       = {10.1613/jair.1.13768},
  url_paper = {https://www.jair.org/index.php/jair/article/view/13768},
  abstract  = {Current domain-independent, classical planners require symbolic models of the problem domain and instance as input, resulting in a knowledge acquisition bottleneck. Meanwhile, although deep learning has achieved significant success in many fields, the knowledge is encoded in a subsymbolic representation which is incompatible with symbolic systems such as planners. We propose Latplan, an unsupervised architecture combining deep learning and classical planning. Given only an unlabeled set of image pairs showing a subset of transitions allowed in the environment (training inputs), Latplan learns a complete propositional PDDL action model of the environment. Later, when a pair of images representing the initial and the goal states (planning inputs) is given, Latplan finds a plan to the goal state in a symbolic latent space and returns a visualized plan execution. We evaluate Latplan using image-based versions of 6 planning domains: 8-puzzle, 15-Puzzle, Blocksworld, Sokoban and Two variations of LightsOut.}
}



@incollection{liu-eai-2022,
  title     = {A Planning based Neural-Symbolic Approach for Embodied Instruction Following},
  author    = {Xiaotian Liu and Hector Palacios and Christian Muise},
  booktitle = {CVPR Embodied AI Workshop},
  year      = {2022},
  url_paper = {https://embodied-ai.org/papers/2022/15.pdf},
  abstract  = {The ALFRED environment features an embodied agent following instructions and accomplishing tasks in simulated home environments. However, end-to-end deep learning methods struggle at these tasks due to long-horizon and sparse rewards. In this work, we propose a principled neural-symbolic approach combining symbolic planning and deep-learning methods for visual perception and NL processing. The symbolic model is enriched as exploration progress until a full plan can be obtained. New perceptions are added to a discrete graph representation that is used for producing new planning problems. Empirical results demonstrate that our approach can achieve high scalability with SOTA performance of 36.04% unseen success rate in the ALFRED benchmark. Our work builds a foundation for a neural-symbolic approach that can act in unstructured environments when the set of skills and possible relationships is known.}
}



@incollection{callanan-icapsdemo-2022,
  title     = {MACQ: A Unified Library for Action Model Acquisition},
  author    = {Ethan Callanan and Rebecca De Venezia and Victoria Armstrong and Alison Paredes and Jungkoo Kang and Tathagata Chakraborti and Christian Muise},
  booktitle = {International Conference on Automated Planning and Scheduling: System Demonstrations},
  year      = {2022},
  url_paper = {http://icaps22.icaps-conference.org/demos/ICAPS_2022_paper_378.pdf},
  abstract  = {For over three decades, the planning community has explored countless methods for data-driven model acquisition. These range in sophistication (e.g., simple set operations to full-blown reformulations), methodology (e.g., logic-based -vs- planning-based), and assumptions (e.g., fully -vs- partially observable). With no fewer than 43 publications in the space, it can be overwhelming to understand what approach could or should be applied in a new setting. We present a holistic characterization of the action model acquisition space and further introduce a unifying framework for automated action model acquisition. We have re-implemented some of the landmark approaches in the area, and our characterization of all the techniques offers deep insight into the research opportunities that remain; i.e., those settings where no technique is capable of solving.}
}



@incollection{coulter-icapsdemo-2022,
  title     = {Planning Tech for Planning Pedagogy},
  author    = {Alex Coulter and Teo Ilie and Renee Tibando and Christian Muise},
  booktitle = {International Conference on Automated Planning and Scheduling: System Demonstrations},
  year      = {2022},
  url_paper = {http://icaps22.icaps-conference.org/demos/ICAPS_2022_paper_376.pdf},
  abstract  = {We demonstrate the power planning techniques can have for the task of analyzing planning solutions in a classroom setting. Using the common assignment strategy of asking students to develop PDDL given an English description of a domain, we consider how a variety of planning methods (existing and new) can provide analytic support for teaching staff to understand which errors were made in student models. The work has already had a direct and practical impact, being deployed in a classroom setting to assess the correctness of student-authored planning models.}
}



@incollection{muise-icapsdemo-2022,
  title     = {PLANUTILS: Bringing Planning to the Masses},
  author    = {Christian Muise and Florian Pommerening and Jendrik Seipp and Michael Katz},
  booktitle = {International Conference on Automated Planning and Scheduling: System Demonstrations},
  year      = {2022},
  url_paper = {http://icaps22.icaps-conference.org/demos/ICAPS_2022_paper_377.pdf},
  abstract  = {PLANUTILS is a general library for setting up Linux-based environments for developing, running, and evaluating planners. Over the last decades, the planning community has produced countless solvers for various planning formalisms, as well as many other tools to help the planning practitioner. From state-of-the-art planners, over validators, to parsing libraries, the planning ecosystem has grown quite large. In the demo, we highlight an effort that aims to unify this ecosystem and make it seamless for users to get started with what the ICAPS community has to offer.}
}



@incollection{simon-spark-2022,
  title     = {TattleTale: Storytelling with Planning and Large Language Models},
  author    = {Nisha Simon and Christian Muise},
  booktitle = {ICAPS Workshop on Scheduling and Planning Applications woRKshop},
  year      = {2022},
  url_paper = {http://icaps22.icaps-conference.org/workshops/SPARK/papers/spark2022_paper_2.pdf},
  abstract  = {We explore how automated planning can be applied to Natural Language text generation in order to create narratives (stories) that are coherent and believable. While Large Language Models (LLMs) such as GPT-3 can be used for narrative generation based on given input prompts, they lack coherence and can be prone to repetition and stilted language. We demonstrate the use of a planning model that provides scaffolding to an LLM so that its language generation is contextdependent in order to create more coherent and believable stories in a variety of domains. After manually extracting characters, objects, and locations from the story source, we create domain and problem encoding that captures the mechanics of the story. The output of a planner, taken one action at a time, is fed to the LLM to generate a narrative. We f ind that almost all nouns (characters, objects, and locations) and verbs (actions) of the plan are reflected in the generated story, and the resulting narrative is more coherent than stories that are generated using only plain text prompts to the LLM. Finally, gathering, curating, and modelling the source stories in PDDL is an additional contribution of our work that will be released publicly. Our work represents a key first step towards the novel application of planning technology to a neuro-symbolic approach for effective story generation}
}



@incollection{coulter-keps-2022,
  title     = {Theory Alignment via a Classical Encoding of Regular Bisimulation},
  author    = {Alex Coulter and Teo Ilie and Renee Tibando and Christian Muise},
  booktitle = {Workshop on Knowledge Engineering for Planning and Scheduling},
  year      = {2022},
  url_paper = {http://icaps22.icaps-conference.org/workshops/KEPS/KEPS-22_paper_7781.pdf},
  abstract  = {Bisimulation, at its core, is a means of studying the alignment between two dynamical systems. It has been used to great effect in the planning community for heuristic computation; simulating the full state space in the space of abstractions (merge-and-shrink heuristics). Here, we consider the direct task of theory alignment– assessing if two planning problems are “equivalent”– through the lens of regular bisimulation. We accomplish the task through a novel encoding that merges the two theories as a new planning problem, where the encoded problem is unsolvable if and only if the two theories are a regular bisimulation. We demonstrate that modern planners are capable of solving many of these encodings, and the solutions (if plans exist) provide a rich explanation as to whytwomodels differ. The work has already had a direct and practical impact, being deployed in a classroom setting to assess the correctness of student-authored planning models as compared against a reference solution. Our solution has a direct impact on being able to verify if a candidate planning model matches a known specification, and opens the door to model verification through planning techniques.}
}



@incollection{cruse-xaip-2022,
  title     = {Discrete Time Series Clustering and Delineation: A Tree-Based Approach to Linear Temporal Logic Discovery},
  author    = {Brennan Cruse and Christian Muise},
  booktitle = {The International Workshop of Explainable AI Planning},
  year      = {2022},
  url_paper = {https://openreview.net/forum?id=nil-578ERCL},
  abstract  = {Inferring temporal logic specifications from plan traces can offer significant insight into several aspects of planning such as goal recognition, policy summarization, and system dynamic modelling. Prior work in this area has predominantly focused on the identification of specifications that satisfy all plan traces within a set, however more recently, contrastive approaches concerning the delineation of two sets have also been established. While these approaches are effective in their defined scope, they assume the existence of only one or two behavioural clusters. In this paper, we re-imagine contrastive specification learning by proposing a novel tree generation technique which allows k clusters to be discovered. By embracing a Monte Carlo node-splitting approach, our algorithm seeks balance to contrastively divide any given set of plan traces into two sets with an accompanying temporal logic specification satisfying one of the sets. Recursing this procedure, we demonstrate the effectiveness of our approach to cluster and delineate plan traces, allowing temporal logic specifications to evoke insight at each level of the resulting tree.}
}



@incollection{sreedharan-xaip-2022,
  title     = {Why Did You Do That? Generalizing Causal Link Explanations to Fully Observable Non-Deterministic Planning Problems},
  author    = {Sarath Sreedharan and Christian Muise and Subbarao Kambhampati},
  booktitle = {The International Workshop of Explainable AI Planning},
  year      = {2022},
  url_paper = {https://openreview.net/forum?id=D44ytXrLXuS},
  abstract  = {The problem of designing automated agents, particularly automated planning agents that can explain their decisions has been receiving a lot of attention in recent years. The field of explainable planning or XAIP has already made a lot of progress in recent years and many of them centered around the problem of explaining decisions derived for classical planning problems. As the field progresses there is interest in tackling problems from more complex planning formalisms. However, one important aspect to keep in mind as we start focusing on such settings is that the explanatory challenges we study in the context of classical planning problems do not disappear when we move to more general settings but are just magnified. As such, when we move to these more general settings, a significant challenge before us is to see how one could generalize the well-established methods studied in the context of classical planning problems to these new settings. To provide a concrete example for this new research program we will start with causal link explanations, one of the earliest and most widely used explanations techniques used in the context of policies generated for fully observable non-deterministic planning problems. This would see us generalizing a concept that was originally developed for a specific solution concept, i.e, sequential plans, and see them applied to a very different solution concept (i.e. policies). We will develop a compilation-based method for generating generalized causal link explanations and show how as the domain is limited to deterministic cases, our method would generate causal link chains as identified by earlier works.}
}



@incollection{klassen-aaai-2022,
  title     = {Planning to Avoid Side Effects},
  author    = {Toryn Q. Klassen and Sheila A. McIlraith and Christian Muise and Jarvis Xu},
  booktitle = {AAAI Conference on Artificial Intelligence},
  pages     = {9830--9839},
  year      = {2022},
  url_paper = {https://ojs.aaai.org/index.php/AAAI/article/view/21219},
  abstract  = {In sequential decision making, objective specifications are often underspecified or incomplete, neglecting to take into account potential (negative) side effects. Executing plans without consideration of their side effects can lead to catastrophic outcomes -- a concern recently raised in relation to the safety of AI. In this paper we investigate how to avoid side effects in a symbolic planning setting. We study the notion of minimizing side effects in the context of a planning environment where multiple independent agents co-exist. We define (classes of) negative side effects in terms of their effect on the agency of those other agents. Finally, we show how plans which minimize side effects of different types can be computed via compilations to cost-optimizing symbolic planning, and investigate experimentally.}
}



@incollection{zhou-eurogp-2022,
  title     = {Permutation-Invariant Representation of Neural Networks with Neuron Embeddings},
  author    = {Ryan Zhou and Christian Muise and Ting Hu},
  booktitle = {European Conference on Genetic Programming},
  pages     = {294–-308},
  year      = {2022},
  url_paper = {https://link.springer.com/chapter/10.1007/978-3-031-02056-8_19},
  abstract  = {Neural networks are traditionally represented in terms of their weights. A key property of this representation is that there are multiple representations of a network which can be obtained by permuting the order of the neurons. These representations are generally not compatible between networks, making recombination a challenge for two arbitrary neural networks - an issue known as the “permutation problem” in neuroevolution. This paper proposes an indirect encoding in which a neural network is represented in terms of interactions between neurons rather than explicit weights, and which works for both fully connected and convolutional networks. In addition to reducing the number of free parameters, this encoding is agnostic to the ordering of neurons, bypassing a key problem for direct weight-based representation. This allows us to transplant individual neurons and layers into another network without accounting for the specific ordering of neurons. We show through experiments on the MNIST and CIFAR-10 datasets that this method is capable of representing networks which achieve comparable performance to direct weight representation, and that combining networks this way preserves a larger degree of performance than through direct weight transfer.}
}



@incollection{muise-icapsjournal-2022,
  title     = {Efficient Multi-agent Epistemic Planning: Teaching Planners About Nested Belief},
  author    = {Christian Muise and Vaishak Belle and Paolo Felli and Sheila McIlraith and Tim Miller and Adrian Pearce and Liz Sonenberg},
  booktitle = {International Conference on Automated Planning and Scheduling: Journal Track},
  year      = {2022},
  url_paper = {https://arxiv.org/pdf/2110.02480.pdf},
  abstract  = {Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology for solving efficiently. Our approach represents an important step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents.}
}



@incollection{klassen-rddps-2022,
  title     = {An AI Safety Threat from Learned Planning Models},
  author    = {Toryn Q. Klassen and Sheila McIlraith and Christian Muise},
  booktitle = {ICAPS Workshop on Reliable Data-Driven Planning and Scheduling},
  year      = {2022},
  url_paper = {http://icaps22.icaps-conference.org/workshops/RDDPS/papers/klassen_etal_rddps22.pdf},
  abstract  = {Historically, planning problems have often been constructed by hand, with the domain model and the goal developed together, leading to the model and goal being in harmony in the sense that the goal describes exactly which parts of the modelled state were desired to be changed (and not changed) as a consequence of the execution of the plan. With models learned from data, human goal specifiers may not know all the aspects of the model, nor have spent much time thinking about the real world situation that is being modelled. Also, naive users may expect the goals they specify to be interpreted in a commonsensical way by the automated planning system. These things may lead human goal specifiers to more often create incomplete goal specifications, failing to take into account all the different ways the environment can be changed– the potential side effects of plans. This could threaten safety. However, learned models may in some cases also have the feature of having detailed state representations, affording the opportunity for symbolic planning algorithms to recognize side effects that their human users did not think of, and to help avoid them. We propose in this position paper that researchers in symbolic planning should take up the challenge of developing planning algorithms that can safely deal with underspecified objectives– i.e., with problem goals that fail to specify everything that people want.}
}



@incollection{nesy-book-2022,
  title={Symbolic Reasoning in Latent Space: Classical Planning as an Example},
  author={Masataro Asai and Hiroshi Kajino and Alex Fukunaga and Christian Muise},
  booktitle={Neuro-Symbolic Artificial Intelligence: The State of the Art},
  pages={52--77},
  year={2022},
  publisher={IOPress},
  url={https://www.iospress.com/catalog/books/neuro-symbolic-artificial-intelligence-the-state-of-the-art},
  abstract={Symbolic systems require hand-coded symbolic representation as input, resulting in a knowledge acquisition bottleneck. Meanwhile, although deep learning has achieved significant success in many fields, the knowledge is encoded in a subsymbolic representation which is incompatible with symbolic systems. To address the gap between the two fields, one has to solve Symbol Grounding problem: The question of how a machine can generate symbols automatically. We discuss our recent work called Latplan, an unsupervised architecture combining deep learning and classical planning. Given only an unlabeled set of image pairs showing a subset of transitions allowed in the environment (training inputs), Latplan learns a complete propositional PDDL action model of the environment. Later, when a pair of images representing the initial and the goal states (planning inputs) is given, Latplan finds a plan to the goal state in a symbolic latent space and returns a visualized plan execution. We discuss several key ideas that made Latplan possible which would hopefully extend to many other symbolic paradigms outside classical planning.}
}





@inproceedings{liu-eai-2021,
  author    = {Xiaotian Liu and Christian Muise},
  title     = {A Neural-Symbolic Approach for Object Navigation},
  booktitle = {2nd Embodied AI Workshop (CVPR 2021)},
  year      = {2021},
  url_paper = {https://embodied-ai.org/papers/A-Neural-Symbolic-Approach-for-Object-Navigation.pdf},
  url_poster = {https://embodied-ai.org/posters/Neural-Symbolic-Approach-for-Object-Navigation.pdf},
  abstract  = {Object navigation refers to the task of discovering and locating objects in an unknown environment. End-to-end deep learning methods struggle at this task due to sparse rewards. In this work, we propose a simple neural-symbolic approach for object navigation in the AI2-THOR environment. Our method takes raw RGB images as input and uses a spatial memory graph as memory to store object and location information. The architecture consists of both a convolutional neural network for object detection and a spatial graph to represent the environment. By having a discrete graph representation of the environment, the agent can directly use search or planning algorithms as high-level reasoning engines. Model performance is evaluated on both task completion rate and steps required to reach target objects. Empirical results demonstrate that our approach can achieve performance close to the optimal. Our work builds a foundation for a neural-symbolic approach that can reason via unstructured visual cues.}
}



@inproceedings{liu-cascon-2021,
  author    = {Xiaotian Liu and Victoria Armstrong and Sara Nabil and Christian Muise},
  title     = {Exploring Multi-View Perspectives on Deep Reinforcement Learning Agents for Embodied Object Navigation in Virtual Home Environments},
  booktitle = {CASCON EVOKE},
  year      = {2021},
  url       = {https://mulab.ai/papers/2021-cascon-liu-armstrong.pdf},
  abstract  = {Recent years have brought the exploration of embodied reinforcement learning agents in a variety of domains. One of the advantages of artificial agents is that they can obtain visual inputs simultaneously using multiple input devices. This work explores multi-view reinforcement learning for object navigation tasks in 3D rendered virtual home environments using AI2-THOR. We trained CNN based Deep Q-learning embodied agents with egocentric, allocentric, and combined egocentric-allocentric perspectives to locate an object in an unknown environment. We compared the results of the three RL agents, and evaluated them by both reward improvement rate, and reward obtained. We demonstrate that the egocentric perspective allows for faster reward accumulation in the earlier episodes, whereas the allocentric agents obtained better long-term rewards. Interesting results arise from the combined allocentric and egocentric perspective, where we found that the agent had the best overall results by harnessing the benefits of each perspective. The results show that while single perspective embodied agents each have their own advantages, combining both inputs yield the best overall reward. Our findings provide a foundation and benchmark for building embodied RL agents with multi-view perspectives.}
}



@inproceedings{simon-keps-2021,
  author    = {Nisha Simon and Christian Muise},
  title     = {A Natural Language Model for Generating PDDL},
  booktitle = {The ICAPS Workshop on Knowledge Engineering for Planning and Scheduling (KEPS)},
  year      = {2021},
  url       = {https://icaps21.icaps-conference.org/workshops/KEPS/Papers/KEPS_2021_paper_7.pdf},
  abstract  = {Language generation in various domains has drawn a large amount of interest in recent years. This paper studies language generation in the context of generating planning specifications in the syntax typically used for this task: the Planning Domain Definition Language (PDDL). The goal of this preliminary work is to predict the next completion in PDDL code, based on previous and surrounding text. Generating valid PDDL code is a key component in creating robust planners. Thus, the ability to generate PDDL code will be extremely useful to PDDL practitioners for the purpose of solving planning problems. It further opens the door to providing a source of inspiration for the modeller. The main contribution of our approach is a language model built using Recurrent Neural Networks (RNNs) that is trained on existing PDDL domains, which can be used to generate PDDL-like code. We train our model on a corpus of publicly available PDDL files from api.planning.domains, and evaluate our approach in the setting of PDDL auto-prediction for some of the more common domains. We found that code-like generation is possible, although fluency can be improved.}
}



@inproceedings{heurviz-icaps21demo,
  author    = {Caitlin Aspinall and Cam Cunningham and Ellie Sekine and Christian Muise},
  title     = {A Planning.Domains Plugin for Heuristic Visualization},
  booktitle = {System Demonstrations at the Thirty-First International Conference on Automated Planning and Scheduling ({ICAPS})},
  year      = {2021},
  url       = {https://icaps21.icaps-conference.org/demos/demos/377.pdf},
  abstract  = {Heuristics are at the heart of every planner. From the simple to the complex, they drive both satisficing and optimal planning approaches. Despite their pervasiveness, there has been relatively little effort towards systematically visualizing them. We introduce a plugin for the online editor at Planning.Domains that is capable of visualizing the heuristic computation of a manually explored state space. Our initial implementation demonstrates the hadd heuristic, but the framework serves as an extensible base for other heuristics in the field of automated planning.}
}



@inproceedings{liu2021intex,
  author    = {Xiaotian Liu and Alison Paredes and Christian Muise},
  title     = {Do You See What I See? An Egocentric View of our Pansophical Planning Problems},
  booktitle = {The ICAPS Workshop on Integrated Planning, Acting, and Execution (IntEx)},
  url       = {https://icaps21.icaps-conference.org/workshops/IntEx/IntEx2021proceedings_final.pdf#page=36},
  abstract  = {Classical planning takes a pansophical view of the world: everything is fully known, observed, and static. While there are extensions to partial observability, this leapfrogs an important intermediate step of embodied agent design: egocentricity. In this work, we propose a semi-automated mechanism that allows planning domain designers to convert classical planning problems into an egocentric alternative. The generated planning problems are classical as well, and we introduce an open-loop replanning mechanism that progressively explores the egocentric space until the original goal is solved (or deemed unsolvable). Our work serves as a crucial first step towards embodied agents that can be equipped with an appropriately specified egocentric version of known environment dynamics.},
  year      = {2021},
}




@article{muise2021aij,
  author    = {Christian Muise and Vaishak Belle and Paolo Felli and Sheila A. McIlraith and Tim Miller and Adrian R. Pearce and Liz Sonenberg},
  title     = {Efficient Multi-agent Epistemic Planning: Teaching Planners About Nested Belief},
  journal   = {Artificial Intelligence Journal},
  year      = {2021},
  url       = {https://arxiv.org/pdf/2110.02480.pdf},
  abstract  = {Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology for solving efficiently. Our approach represents an important step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents.}
}



@incollection{muise2021keps,
  title={KEPS Book: Planning. Domains},
  author={Muise, Christian and Lipovetzky, Nir},
  booktitle={Knowledge Engineering Tools and Techniques for AI Planning},
  pages={91--105},
  year={2021},
  publisher={Springer}
}



@inproceedings{zhang-keps-2020,
  author    = {Qianyu Zhang and Christian Muise},
  title     = {Action Usability via Deadend Detection},
  booktitle = {The ICAPS Workshop on Knowledge Engineering for Planning and Scheduling (KEPS)},
  year      = {2020},
  abstract  = {The vast majority of planning problem models are incorrect, incomplete, or simply inconsistent. This is particularly an issue during the development process of a planning model. However, there are few existing debugging tools for modeling to aid with this issue. In this paper, we introduce a method for detecting actions that are deemed unusable, which may naturally be a result of modelling errors. We use action usability (or reachability) detection for on-the-fly diagnostics of planning models. In our technique, each action is checked for usability via problem reformulation and unsolvability detection. Through the analysis of usability, this technique could improve the modeling process directly, and we have demonstrated this capability through the tight integration with the online PDDL editor at Planning.Domains.}
}



@inproceedings{betech-itsc-2020,
  author = {Salomon Wollenstein-Betech and Christian Muise and Christos G. Cassandrass and Ioannis Ch. Paschalidis and Yasaman Khazaeni},
  title = {Explainability of Intelligent Transportation Systems using Knowledge Compilation: a Traffic Light Controller Case},
  booktitle = {The 23rd IEEE International Conference on Intelligent Transportation Systems},
  year = {2020},
  url = {https://arxiv.org/pdf/2007.04916.pdf},
  abstract = {Usage of automated controllers which make decisions on an environment are widespread and are often based on black-box models. We use Knowledge Compilation theory to bring explainability to the controllers decision given the state of the system. For this, we use simulated historical state action data as input and build a compact and structured representation which relates states with actions. We implement this method in a Traffic Light Control scenario where the controller selects the light cycle by observing the presence (or absence) of vehicles in different regions of the incoming roads.},
}



@inproceedings{asai-ijcai-2020,
  author = {Masataro Asai and Christian Muise},
  title = {Learning Neural-Symbolic Descriptive Planning Models via Cube-Space Priors: The Voyage Home (to STRIPS)},
  booktitle = {29th International Joint Conference on Artificial Intelligence},
  url = {https://arxiv.org/pdf/2004.12850.pdf},
  abstract = {We achieved a new milestone in the difficult task of enabling agents to learn about their environment autonomously. Our neuro-symbolic architecture is trained end-to-end to produce a succinct and effective discrete state transition model from images alone. Our target representation (the Planning Domain Definition Language) is already in a form that off-the-shelf solvers can consume, and opens the door to the rich array of modern heuristic search capabilities. We demonstrate how the sophisticated innate prior we place on the learning process significantly reduces the complexity of the learned representation, and reveals a connection to the graphtheoretic notion of cube-like graphs, thus opening the door to a deeper understanding of the ideal properties for learned symbolic representations. We show that the powerful domain-independent heuristics allow our system to solve visual 15-Puzzle instances which are beyond the reach of blind search, without resorting to the Reinforcement Learning approach that requires a huge amount of training on the domain-dependent reward information.},
  year = {2020},
}



@inproceedings{sreedharan-icaps20,
  author    = {Sarath Sreedharan and Tathagata Chakraborti and Christian Muise and Yasaman Khazaeni and Subbarao Kambhampati},
  title     = {{D3WA+}: A Case Study of {XAIP} in a Model Acquisition Task},
  booktitle = {Proceedings of the Thirtieth International Conference on Automated Planning and Scheduling ({ICAPS})},
  year      = {2020},
  abstract  = {Recently, the D3WA system was proposed as a paradigm shift in how complex goal-oriented dialogue agents can be specified by taking a declarative view of design. However, it turns out actual users of the system have a hard time evolving their mental model and grasping the imperative consequences of declarative design. In this paper, we adopt ideas from existing works in the field of Explainable AI Planning (XAIP) to provide guidance to the dialogue designer during the model acquisition process. We will highlight in the course of this discussion how the setting presents unique challenges to the XAIP setting, including having to deal with a different user persona as the domain modeler rather than the end-user of the system, and consequently having to deal with the unsolvability of models in addition to explaining generated plans.}
}




@misc{muise2019planning,
    title={Planning for Goal-Oriented Dialogue Systems},
    author={Christian Muise and Tathagata Chakraborti and Shubham Agarwal and Ondrej Bajgar and Arunima Chaudhary and Luis A. Lastras-Montano and Josef Ondrej and Miroslav Vodolan and Charlie Wiecha},
    year={2019},
    eprint={1910.08137},
    archivePrefix={arXiv},
    primaryClass={cs.AI},
    url={https://arxiv.org/pdf/1910.08137},
    abstract={Generating complex multi-turn goal-oriented dialogue agents is a difficult problem that has seen a considerable focus from many leaders in the tech industry, including IBM, Google, Amazon, and Microsoft. This is in large part due to the rapidly growing market demand for dialogue agents capable of goal-oriented behaviour. Due to the business process nature of these conversations, end-to-end machine learning systems are generally not a viable option, as the generated dialogue agents must be deployable and verifiable on behalf of the businesses authoring them. In this work, we propose a paradigm shift in the creation of goal-oriented complex dialogue systems that dramatically eliminates the need for a designer to manually specify a dialogue tree, which nearly all current systems have to resort to when the interaction pattern falls outside standard patterns such as slot filling. We propose a declarative representation of the dialogue agent to be processed by state-of-the-art planning technology. Our proposed approach covers all aspects of the process; from model solicitation to the execution of the generated plans/dialogue agents. Along the way, we introduce novel planning encodings for declarative dialogue synthesis, a variety of interfaces for working with the specification as a dialogue architect, and a robust executor for generalized contingent plans. We have created prototype implementations of all components, and in this paper, we further demonstrate the resulting system empirically.}
}






@inproceedings{muise-pair20,
  title={Modeling Blackbox Agent Behaviour via Knowledge Compilation},
  author={Christian Muise and Salomón Wollenstein-Betech and Serena Booth and Julie Shah and Yasaman Khazaeni},
  booktitle={The AAAI 2020 Workshop on Plan, Activity, and Intent Recognition},
  year={2020},
  abstract={Understanding how agents behave in an environment is a corner-stone for interpretable AI. In this work, we focus on capturing the policy an agent is following without placing any assumptions on how that policy is actually implemented. From a corpus of state-action pairs, our task is to build a compact and diagnosable representation of the mapping from states to actions. We appeal to modern knowledge compilation techniques for this task and demonstrate empirically how this approach outperforms the previous state of the art. We further create an interactive inference on the compiled representation to get an intuitive sense of the policy.}
}



@inproceedings{sreedharan-aaai20,
  title={Expectation-Aware Planning: A General Framework for Synthesizing and Executing Self-Explaining Plans for Human-AI Interaction},
  author={Sarath Sreedharan and Tathagata Chakraborti and Christian Muise and Subbarao Kambhampati},
  booktitle={The 34th AAAI Conference on Artificial Intelligence},
  year={2020},
  abstract={In this work, we present a new planning formalism called Expectation-Aware planning for decision making with humans in the loop where the human's expectations about an agent may differ from the agent's own model. We show how this formulation allows agents to not only leverage existing strategies for handling model differences like explanations and explicability, but can also exhibit novel behaviors that are generated through the combination of these different strategies. Our formulation also reveals a deep connection to existing approaches in epistemic planning. Specifically, we show how we can leverage classical planning compilations for epistemic planning to solve Expectation-Aware planning problems. To the best of our knowledge, the proposed formulation is the first complete solution to decision-making in the presence of diverging user expectations that is amenable to a classical planning compilation while successfully combining previous works on explanation and explicability. We empirically show how our approach provides a computational advantage over our earlier approaches that rely on search in the space of models.}
}



@inproceedings{agarwal-aaaidemo20,
  author={Shubham Agarwal and Christian Muise and Mayank Agarwal and Sohini Upadhyay and Zilu Tang and Zhongshen Zeng and Yasaman Khazaeni},
  title={TraceHub -- A Platform to bridge the gap between State-Of-The-Art Time-Series Analytics and Datasets},
  booktitle={AAAI 2020 System Demonstration Track},
  year={2020},
  bibbase_note = {<span style="color: purple">Best System Demonstration Award.</span>},
  abstract={In this paper, we present TraceHub - a platform that connects new non-trivial state-of-the-art time-series analytics with datasets from different domains. Analytics owners can run their insights on new datasets in an automated setting to find insight's potential and improve it. Dataset owners can find all possible types of non-trivial insights based on latest research. We provide a plug-n-play system as a set of Dataset, Transformer pipeline, and Analytics APIs for both kinds of users. We show a usefulness measure of generated insights across various types of analytics in the system. We believe that this platform can be used to bridge the gap between time-series analytics and datasets by significantly reducing the time to find the true potential of budding time-series research and improving on it faster.}
}




@inproceedings{vukovic-icsoc19,
  title={Towards Automated AI Planning for Enterprise Services: Opportunities and Challenges},
  author={Maja Vukovic and Scott Gerard and Michael Katz and Laura Shwartz and Shirin Sohrabi and Christian Muise and John Rofrano and Anup Kalia and Jinho Hwang and Ya Bin Dang and Jie DJ Ma and Zhuo Xuan Jiang},
  booktitle={The 17th International Conference on Service-Oriented Computing (ICSOC-2019)},
  year={2019},
  url={https://link.springer.com/chapter/10.1007%2F978-3-030-33702-5_6},
  abstract={Existing Artificial Intelligence (AI) driven automation solutions in enterprises employ machine learning, natural language processing, and chatbots. There is an opportunity for AI Planning to be applied, which offers reasoning about action trajectories to help build automation blueprints. AI Planning is a problem-solving technique, where knowledge about available actions and their consequences is used to identify a sequence of actions, which, when applied in a given initial state, satisfy a desired goal. AI Planning has successfully been applied in a number of domains ranging from space applications, logistics and transportation, manufacturing, robotics, scheduling, e-learning, enterprise risk management, and service composition. In this paper, we discuss experience in building automation solutions that employ AI planning for use in enterprise IT and business services, such as change and event management, migration and transformation and RPA composition. We discuss challenges in adoption of AI planning across the enterprise from implementation and deployment perspectives.}
}



@inproceedings{kim-ijcai19,
  title={Bayesian Inference of Temporal Specifications to Explain How Plans Differ},
  author={Joseph Kim and Christian Muise and Ankit Shah and Shubham Agarwal and Julie Shah},
  booktitle={The 28th International Joint Conference on Artificial Intelligence (IJCAI)},
  year={2019},
  url={https://www.ijcai.org/proceedings/2019/0776.pdf},
  abstract={Temporal logics are useful for describing dynamic system behavior, and have been successfully used as a language for goal definitions during task planning. Prior works on inferring temporal logic specifications have focused on ``summarizing'' the input dataset -- i.e., finding specifications that are satisfied by all plan traces belonging to the given set. In this paper, we examine the problem of inferring specifications that describe temporal differences between two sets of plan traces. We formalize the concept of providing such \emph{contrastive} explanations, then present a Bayesian probabilistic model for inferring contrastive explanations as linear temporal logic specifications. We demonstrate the efficacy, scalability, and robustness of our model for inferring correct specifications across various benchmark planning domains and for a simulated air combat mission.}
}



@inproceedings{booth-ijcai19,
  title={Evaluating the Interpretability of the Knowledge Compilation Map: Communicating Logical Statements Effectively},
  author={Serena Booth and Christian Muise and Julie Shah},
  booktitle={The 28th International Joint Conference on Artificial Intelligence (IJCAI)},
  year={2019},
  url={https://www.ijcai.org/proceedings/2019/0804.pdf},
  abstract={Knowledge compilation techniques translate propositional theories into equivalent forms to increase their computational tractability. But, how should we best present these propositional theories to a human? We analyze the standard taxonomy of propositional theories for relative \emph{interpretability} across three model domains: highway driving, emergency triage, and the chopsticks game. We generate decision-making agents which produce logical explanations for their actions and apply knowledge compilation to these explanations. Then, we evaluate how quickly, accurately, and confidently users comprehend the generated explanations to make decisions. We find that domain, formula size, and negated logical connectives significantly affect comprehension while formula properties typically associated with interpretability are not strong predictors of human ability to comprehend the theory.}
}



@inproceedings{sreedharan-xaip19,
  title={A General Framework for Synthesizing and Executing Self-Explaining Plans for Human-AI Interaction},
  author={Sarath Sreedharan and Tathagata Chakraborti and Christian Muise and Subbarao Kambhampati},
  booktitle={The 2nd ICAPS Workshop on Explainable Planning (XAIP-2019)},
  year={2019},
  url={https://openreview.net/pdf?id=H1gyE6hm5V},
  abstract={In this work, we present a general formulation for decision making in human-in-the-loop planning problems where the human’s expectations about an autonomous agent may differ from the agent’s own model. We show how our formulation for such multi-model planning problems allows us to capture existing approaches to this problem and also be used to generate novel explanatory behaviors. Our formulation also reveals a deep connection between multi-model planning and epistemic planning and we show how we can leverage classical planning compilations designed for epistemic planning for solving multi-model planning problems. We empirically show how this new compilation provides a computational advantage over previous approaches that separate reasoning about model reconciliation and identifying the agent’s plan.}
}



@inproceedings{muise-intex19,
  title={Executing Contingent Plans: Addressing Challenges in Deploying Artificial Agents},
  author={Christian Muise and Miroslav Vodolan and Shubham Agarwal and Ondrej Bajgar and Luis Lastras and Josef Ondrej},
  booktitle={ICAPS Workshop on Integrating Planning, Acting, and Execution (IntEx)},
  year={2019},
  url = {https://icaps19.icaps-conference.org/workshops/IntEx/IntEx-2019-proceedings.pdf#page=32},
  abstract={The vast majority of research in automated planning focuses on generating a plan from an initial problem specification; from the theoretical properties of this task to the implementation details required to do so efficiently. While such work is often motivated by practical applications, there is far less understanding of the issues associated with executing plans in online environments. In this work we focus on this understudied area, and the challenges / opportunities that arise when executing complex plans. Unlike many works in plan execution, we consider a form of contingent plans as the source for execution; their complexity stems from the sophisticated representation of the action effects used to model the uncertainty in the world. The key contribution of our work is a proposed executor that can reason using the sophisticated action effects, and we demonstrate the impact this can have empirically. In support of an effective executor, we also consider (1) the connection between the execution context and the planner's view of the state of the world; and (2) the separation between the execution of an action (the aspect that affects the outside environment) and the realization of its effects (the aspect that captures what has actually changed).}
}



@book{pddlbook,
  author    = {Patrik Haslum and Nir Lipovetzky and Daniele Magazzeni and Christian Muise},
  title     = {An Introduction to the Planning Domain Definition Language},
  publisher = {Morgan \& Claypool},
  year      = {2019},
  isbn      = {9781627058759},
  url       = {http://www.morganclaypoolpublishers.com/catalog_Orig/product_info.php?products_id=1384},
  abstract  = {Planning is the branch of Artificial Intelligence (AI) that seeks to automate reasoning about plans, most importantly the reasoning that goes into formulating a plan to achieve a given goal in a given situation. AI planning is model-based: a planning system takes as input a description (or model) of the initial situation, the actions available to change it, and the goal condition to output a plan composed of those actions that will accomplish the goal when executed from the initial situation.

The Planning Domain Definition Language (PDDL) is a formal knowledge representation language designed to express planning models. Developed by the planning research community as a means of facilitating systems comparison, it has become a de-facto standard input

language of many planning systems, although it is not the only modelling language for planning. Several variants of PDDL have emerged that capture planning problems of different natures and complexities, with a focus on deterministic problems. The purpose of this book is two-fold. First, we present a unified and current account of PDDL, covering the subsets of PDDL that express discrete, numeric, temporal, and hybrid planning. Second, we want to introduce readers to the art of modelling planning problems in this language, through educational examples that demonstrate how PDDL is used to model realistic planning problems. The book is intended for advanced students and researchers in AI who want to dive into the mechanics of AI planning, as well as those who want to be able to use AI planning systems without an in-depth explanation of the algorithms and implementation techniques they use.}
}



@inproceedings{pdeditor-icaps19demo,
  author    = {Jan Dolejsi and Derek Long and Maria Fox and Christian Muise},
  title     = {From a Classroom to an Industry From PDDL “Hello World” to Debugging a Planning Problem},
  booktitle = {System Demonstrations at the Twenty-Ninth International Conference on Automated Planning and Scheduling ({ICAPS})},
  year      = {2019},
  url       = {http://www.haz.ca/papers/icaps-demo-dolejsi-19.pdf},
  abstract  = {This demonstration will build on previously shown end-to-end PDDL2.2 developer environ-ment built on top of Microsoft’s VS Code, but will expand substantially in two directions. First, the session from editor.planning.domains can seam-lessly migrate from the web browser to VS Code editor and take advantage of more elaborate PDDL support. Second, VS Code now integrates debugging tools for PDDL modelers such as search tree visualization, step-by-step search de-bugger and plan validation. This offers coherent set of tools to use from classroom to an industrial planning application debugging.}
}



@inproceedings{mai-icaps19demo,
  author    = {Tathagata Chakraborti and Christian Muise and Shubham Agarwal and Luis A. Lastras and Yasaman Khazaeni},
  title     = {{MAi}: An Interface for Declarative Specification of Goal-Directed Dialogue Agents},
  booktitle = {System Demonstrations at the Twenty-Eighth International Conference on Automated Planning and Scheduling ({ICAPS})},
  year      = {2019},
  url       = {http://www.haz.ca/papers/icaps-demo-chakraborti-19.pdf},
  abstract  = {The state of the art of dialogue agents requires a lengthy design process spanning months with experts in the loop who specify complex conversation patterns manually. Our work proposes a paradigm shift in bot design by adopting a declarative approach which composes the full dialog tree automatically. This allows the designer to construct complex dialogue agents from scratch and interact with them in a matter of hours. The demonstration will allow the audience to interact with this new design paradigm and construct their own bots on the spot.}
}



@inproceedings{botea2019deepdial,
  title={Generating Dialogue Agents via Automated Planning},
  author={Adi Botea and Christian Muise and Shubham Agarwal and Oznur Alkan and Ondrej Bajgar and Elizabeth Daly and Akihiro Kishimoto and Luis Lastras and Radu Marinescu and Josef Ondrej and Pablo Pedemonte and Miroslav Vodolan},
  booktitle={The Second AAAI Workshop On Reasoning And Learning For Human-Machine Dialogues (DEEP-DIAL 2019)},
  year={2019},
  url={http://www.haz.ca/papers/deepdial-botea-19.pdf},
  abstract={Dialogue systems have many applications such as customer support or question answering. Typically they have been limited to shallow single turn interactions. However more advanced applications such as career coaching or planning a trip require a much more complex multi-turn dialogue. Current limitations of conversational systems have made it difficult to support applications that require personalization, customization and context dependent interactions. We tackle this challenging problem by using domain-independent AI planning to automatically create dialogue plans, customized to guide a dialogue towards achieving a given goal. The input includes a library of atomic dialogue actions, an initial state of the dialogue, and a goal. Dialogue plans are plugged into a dialogue system capable to orchestrate their execution. Use cases demonstrate the viability of the approach. Our work on dialogue planning has been integrated into a product, and it is in the process of being deployed into another.}
}



@inproceedings{mai-aaai19demo,
  author    = {Tathagata Chakraborti and Christian Muise and Shubham Agarwal and Luis A. Lastras},
  title     = {{MAi}: An Intelligent Model Acquisition Interface for Interactive Specification of Dialog Agents},
  booktitle = {AAAI 2019 System Demonstration Track},
  year      = {2019},
  url       = {http://www.haz.ca/papers/aaai-demo-chakraborti-19.pdf},
  abstract  = {The state of the art in automated conversational agents for enterprise (e.g. for customer support) require a lengthy design process with experts in the loop who have to figure out and specify complex conversation patterns. This demonstration looks at a prototype interface that aims to bring down the expertise required to design such agents as well as the time taken to do so. Specifically, we will focus on how a metawriter can assist the domain-writer during the design process and how complex conversation patterns can be derived from simplifying abstractions at the interface level.}
}



@inproceedings{muise2018sip,
  title={Executing Contingent Plans: Challenges in Deploying Artificial Agents},
  author={Christian Muise and Miroslav Vodolan and Shubham Agarwal and Ondrej Bajgar and Luis Lastras},
  booktitle={Fall Symposium on Integrating Planning, Diagnosis, and Causal Reasoning},
  year={2018},
  url={http://www.haz.ca/papers/sip-muise-18.pdf},
  abstract={The vast majority of research in automated planning focuses on generating a plan from an initial problem specification, from the theoretical properties of this task to the implementation details required to do so efficiently. While such work is often motivated by practical applications, there is far less understanding of the issues associated with executing plans in online environments. In this work, we focus on some of the key challenges of executing contingent plans in a real-world deployment, and present some possible solutions.}
}



@inproceedings{muise2018drde,
  title={Characterizing and Computing All Delete-Relaxed Dead-ends},
  author={Christian Muise},
  booktitle={Workshop on Constraint Satisfaction Techniques for Planning and Scheduling Problems (COPLAS'18)},
  year={2018},
  url={http://www.haz.ca/papers/coplas-muise-18.pdf},
  abstract={Dead-end detection is a key challenge in automated planning,
and it is rapidly growing in popularity. Effective dead-end
detection techniques can have a large impact on the strength
of a planner, and so the effective computation of dead-ends is
central to many planning approaches. One of the better understood
techniques for detecting dead-ends is to focus on the
delete relaxation of a planning problem, where dead-end detection
is a polynomial-time operation. In this work, we provide
a logical characterization for not just a single dead-end,
but for every delete-relaxed dead-end in a planning problem.
With a logical representation in hand, one could compile the
representation into a form amenable to effective reasoning.
We lay the ground-work for this larger vision and provide a
preliminary evaluation to this end.}
}



@inproceedings{koenig2018grw,
  title={Non-Traditional Objective Functions for MDPs},
  author={Sven Koenig and Christian Muise and Scott Sanner},
  booktitle={6th Goal Reasoning Workshop},
  year={2018},
  url={http://www.haz.ca/papers/grw-koenig-18.pdf},
  abstract={While probabilistic planning in AI research has
largely focused on cost-optimal goal-based objectives,
we argue that many realistic planning problems
require more complex objective functions and
different perspectives on goals than is commonly
found in the literature. In this paper, we try to understand
the existing focus of probabilistic planning
on cost-optimal goal-based objectives, then
we proceed to outline some early and current AI research
on non-traditional objectives. We conclude
by charging AI researchers to focus more on realistic
objectives to make probabilistic planning more
attractive for actual applications.}
}



@inproceedings{bhar-vaq-wil-mui-ijcai18,
  author    = {Nikhil Bhargava and
               Tiago Vaquero and
               Brian Williams and
               Christian Muise},
  title     = {Managing Communication Costs under Temporal Uncertainty},
  booktitle = {Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ({IJCAI})},
  year      = {2018},
  url       = {https://www.ijcai.org/proceedings/2018/0648.pdf},
  abstract  = {In multi-agent temporal planning, individual agents cannot know a priori when other agents will execute their actions and so treat those actions as uncertain. Only when others communicate the results of their actions can that uncertainty be resolved. If a full communication protocol is specified ahead of time, then delay controllability can be used to assess the feasibility of the temporal plan. However, agents often have flexibility in choosing when to communicate the results of their action. In this paper, we address the question of how to choose communication protocols that guarantee the feasibility of the original temporal plan subject to some cost associated with that communication. To do so, we introduce a means of extracting delay controllability conflicts and show how we can use these conflicts to more efficiently guide our search. We then present three conflict-directed search algorithms and explore the theoretical and empirical tradeoffs between the different approaches.}
}



@inproceedings{bhar-wil-mui-ijcai18,
  author    = {Nikhil Bhargava and
               Brian Williams and
               Christian Muise},
  title     = {Variable-Delay Controllability},
  booktitle = {Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ({IJCAI})},
  year      = {2018},
  abstract  = {In temporal planning, agents must schedule a set of events satisfying a set of predetermined constraints. These scheduling problems become more difficult when the duration of certain actions are outside the agent's control. Delay controllability is the generalized notion of whether a schedule can be constructed in the face of uncertainty if the agent eventually learns when events occur. Our work introduces the substantially more complex setting of determining variable-delay controllability, where an agent learns about events after some unknown but bounded amount of time has passed. We provide an efficient O(n^3) variable-delay controllability checker and show how to create an execution strategy for variable-delay controllability problems. We also provide preliminary empirical evaluations of the quality of variable-delay controllability results as compared to approximations that use fixed delays to model the same problems. To our knowledge, these essential capabilities are absent from existing controllability checking algorithms.}
}



@inproceedings{cam-mui-bai-mci-ijcai18,
  author    = {Alberto Camacho and
               Christian Muise and
               Jorge A. Baier and
               Sheila A. McIlraith},
  title     = {{LTL} Realizability via Safety and Reachability Games},
  booktitle = {Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ({IJCAI})},
  year      = {2018},
  url_paper = {https://www.ijcai.org/proceedings/2018/0651.pdf},
  url_poster = {https://www.cs.toronto.edu/~acamacho/papers/cam-mui-bai-mci-ijcai18-poster.pdf},
  abstract  = {LTL synthesis is the problem of computing a strategy that satisfies a given property expressed in Linear Temporal Logic (LTL). Synthesis provides a means of constructing programs that allow an agent to interact with their environment following a declarative specification of behavior. Modern approaches to LTL synthesis exploit bounded synthesis techniques, reducing the synthesis problem to a series of safety games between the agent and the environment. The same techniques can be used to determine unrealizability of LTL specifications by solving dual games where the order of turn taking is inverted. In the first part of the paper, we investigate the role of this duality in LTL realizability and synthesis. We describe different reductions to automata games that exploit duality to determine realizability, and introduce novel techniques that reduce realizability to a series of reachability games. In the second part of the paper, we introduce algorithms to solve these safety and reachability games via Fully Observable Non-Deterministic (FOND) planning. Our experimental evaluation illustrates that, by reducing the problem to a reachability game we can solve some problems that state-of-the-art synthesis tools cannot solve. Moreover, it shows that planning can be a competitive approach to LTL synthesis and realizability.}
}



@inproceedings{cam-mui-bai-mci-ijcai18demo,
  author    = {Alberto Camacho and
               Christian Muise and
               Jorge A. Baier and
               Sheila A. McIlraith},
  title     = {{SynKit}: {LTL} Synthesis as a Service},
  booktitle = {Demonstration Track at the Twenty-Seventh International Joint Conference on Artificial Intelligence ({IJCAI})},
  year      = {2018},
  url_paper = {https://www.ijcai.org/proceedings/2018/0848.pdf},
  url_link  = {https://www.cs.toronto.edu/~acamacho/synkit},
  abstract  = {Automatic synthesis of software from specification is one of the classic problems in computer science. In the last decade, significant advances have been made in the synthesis of programs from specifications expressed in Linear Temporal Logic (LTL). LTL synthesis technology is central to a myriad of applications from the automated generation of controllers for Internet of Things devices, to the synthesis of control software for robotic applications. Unfortunately, the number of existing tools for LTL synthesis is limited, and using them requires specialized expertise. In this paper we present SynKit, a tool that offers LTL synthesis as a service. SynKit integrates a RESTful API and a web service with an editor, a solver, and a strategy visualizer.}
}



@inproceedings{cam-mui-bai-mci-icaps18demo,
  author    = {Alberto Camacho and
               Christian Muise and
               Jorge A. Baier and
               Sheila A. McIlraith},
  title     = {{SynKit}: Finite {LTL} Synthesis as a Service},
  booktitle = {System Demonstrations at the Twenty-Eighth International Conference on Automated Planning and Scheduling ({ICAPS})},
  year      = {2018},
  url_paper = {http://icaps18.icaps-conference.org/fileadmin/alg/conferences/icaps18/files/demos/2_SynKit_Demo_ICAPS_18.pdf},
  url_link = {https://www.cs.toronto.edu/~acamacho/synkit},
  bibbase_note = {<span style="color: purple">Best System Demonstration Award.</span>},
  abstract = {Automatic synthesis of software from specification is one of the classical problems in computer science. Recent research has explored the use of finite linear temporal logic (LTLf) as a specification language. Engineers, researchers, and practitioners who wish to explore LTLf synthesis must overcome several barriers, including the lack of convenient tools to synthesize programs. In this paper we present SynKit, a web service that provides an LTLf synthesis capability. SynKit aims to simplify the task of synthesizing programs and debugging specifications. Offered as a web service, it is very accessible and does not require installation. SynKit integrates an editor, a solver, and a strategy visualizer.}
}



@inproceedings{cam-bai-mui-mci-icaps18,
  author    = {Alberto Camacho and
               Jorge A. Baier and
               Christian Muise and
               Sheila A. McIlraith},
  title     = {Finite {LTL} Synthesis as Planning},
  booktitle = {Proceedings of the Twenty-Eight International Conference on Automated Planning and Scheduling ({ICAPS})},
  year      = {2018},
  url       = {http://www.cs.toronto.edu/~acamacho/papers/cam-bai-mui-mci-icaps18.pdf},
  abstract  = {LTL synthesis is the task of generating a strategy that satisfies a Linear Temporal Logic (LTL) specification interpreted over infinite traces. In this paper we examine the problem of LTLf synthesis, a variant of LTL synthesis where the specification of the behaviour of the strategy we generate is interpreted over finite traces – similar to the assumption we make in many planning problems, and important for the synthesis of business processes and other system interactions of finite duration. Existing approaches to LTLf synthesis transform LTLf into deterministic finite-state automata (DFA) and reduce the synthesis problem to a DFA game. Unfortunately, the DFA transformation is worst-case double-exponential in the size of the formula, presenting a computational bottleneck. In contrast, our approach exploits non-deterministic automata, and we reduce the synthesis problem to a non-deterministic planning problem. We leverage our approach not only for strategy generation but also to generate certificates of unrealizability – the first such method for LTLf. We employ a battery of techniques that exploit the structure of the LTLf specification to improve the efficiency of our transformation to automata. We combine these techniques with lazy determinization of automata and on-the-fly state abstraction. We illustrate the effectiveness of our approach on a set of established LTL synthesis benchmarks adapted to finite LTL.}
}



@inproceedings{cam-bai-mui-mci-ccai18,
  author    = {Alberto Camacho and
               Jorge A. Baier and
               Christian Muise and
               Sheila A. McIlraith},
  title     = {Synthesizing Controllers: On the Correspondence Between {LTL} Synthesis and Non-deterministic Planning},
  booktitle = {Advances in Artificial Intelligence - Proceedings of the Thirty-First Canadian Conference on Artificial Intelligence ({CCAI})},
  pages     = {45--59},
  year      = {2018},
  url       = {https://doi.org/10.1007/978-3-319-89656-4_4},
  url_paper = {http://www.cs.toronto.edu/~acamacho/papers/cam-bai-mui-mci-cai2018.pdf},
  abstract  = {Linear Temporal Logic (LTL) synthesis can be understood as the problem of building a controller that defines a winning strategy, for a two-player game against the environment, where the objective is to satisfy a given LTL formula. It is an important problem with applications in software synthesis, including controller synthesis. In this paper we establish the correspondence between LTL synthesis and fully observable non-deterministic (FOND) planning. We study LTL interpreted over both finite and infinite traces. We also provide the first explicit compilation that translates an LTL synthesis problem to a FOND problem. Experiments with state-of-the-art LTL FOND and synthesis solvers show automated planning to be a viable and effective tool for highly structured LTL synthesis problems.}
}



@inproceedings{Chen2018,
 address = {New Orleans, LA},
 author = {Jingkai Chen and Cheng Fang and Christian Muise and Peng Yu and Howard E. Shrobe and Brian C. Williams},
 booktitle = {AAAI Workshop on Artificial Intelligence for Cyber Security (AICS-2018)},
 title = {RADMAX: Risk and Deadline Aware Planning for Maximum Utility},
 year = {2018},
 url = {http://groups.csail.mit.edu/mers/publication_uploads/Publications/2018/AAAI18Chen/radmaxAAAI18.pdf},
 abstract = {Current  network  approaches  aim  to  maximize  network  utilization when routing flows. While such approaches are fast
and usually result in acceptable behavior, existing methods are not
mission aware. There is no concept of utility maximization, no capability to handle flows with specified deadlines and loss requirements, and no guarantees over the probability of network saturation.
In this paper, we present RADMAX: a system for Risk And
Deadline Aware Planning for Maximum Utility based on constraint programming, which allows us to handle higher level
mission  specifications.  We  show  the  correctness  of  RADMAX with respect to loss and delay bounds, provide results
for the optimality of RADMAX with respect to the mission
utility,  and  review  current  results  on  computational  performance}
}



@inproceedings{camacho-genplan-17,
  title = {Bridging the Gap Between LTL Synthesis and Automated Planning},
  author = {Alberto Camacho and Jorge Baier and Christian Muise and Sheila A. McIlraith},
  booktitle = {Workshop on Generalized Planning ({GenPlan}'17)},
  year = {2017},
  url = {http://www.cs.toronto.edu/~sheila/publications/cam-etal-genplan17.pdf},
  abstract={Linear Temporal Logic (LTL) synthesis can be understood as the problem of building a controller that defines a winning strategy, for a two-player game against the environment, where the objective is to satisfy a given LTL formula. It is an important problem with applications in software synthesis, including controller synthesis. Recent work has explored the close connection between automated planning and LTL synthesis but has not provided a full mapping between the two problems nor have its practical implications been explored. In this paper we establish the correspondence between LTL synthesis and fully observable non-deterministic (FOND) planning. We also provide the first explicit compilation that translates an LTL synthesis problem to a FOND problem. Experiments with state-of-the-art LTL FOND and synthesis solvers show automated planning to be a viable and effective tool for highly structured LTL synthesis problems.}
}




@inproceedings{bfr-aaai-17,
  author = {Brent Mombourquette and Christian Muise and Sheila McIlraith},
  year = {2017},
  booktitle = {The 31st AAAI Conference on Artificial Intelligence},
  keywords = {state estimation, belief tracking, logical filtering, approximation},
  title = {Logical Filtering and Smoothing: State Estimation in Partially Observable Domains},
  url = {http://www.haz.ca/papers/momb-aaai17.pdf},
  abstract = {State estimation is the task of estimating the state of a partially
observable dynamical system given a sequence of executed
actions and observations. In logical settings, state estimation
can be realized via logical filtering, which is exact
but can be intractable. We propose logical smoothing, a form
of backwards reasoning that works in concert with approximated
logical filtering to refine past beliefs in light of new observations.
We characterize the notion of logical smoothing
together with an algorithm for backwards-forwards state estimation.
We also present an approximation of our smoothing
algorithm that is space efficient. We prove properties of our
algorithms, and experimentally demonstrate their behaviour,
contrasting them with state estimation methods for planning.
Smoothing and backwards-forwards reasoning are important
techniques for reasoning about partially observable dynamical
systems, introducing the logical analogue of effective
techniques from control theory and dynamic programming.}
}



@inproceedings{ltl-fond-aaai-17,
  author = {Alberto Camacho and Eleni Triantafillou and Christian Muise and Jorge Baier and Sheila A. McIlraith},
  year = {2017},
  booktitle = {The 31st AAAI Conference on Artificial Intelligence},
  keywords = {LTL, non-deterministic planning, temporally extended goals, FOND},
  title = {Non-Deterministic Planning with Temporally Extended Goals: {LTL} over finite and infinite traces},
  url = {http://www.haz.ca/papers/camacho-aaai17.pdf},
  abstract = {Temporally extended goals are critical to the specification of a
diversity of real-world planning problems. Here we examine
the problem of non-deterministic planning with temporally
extended goals specified in linear temporal logic (LTL), interpreted
over either finite or infinite traces. Unlike existing
LTL planners, we place no restrictions on our LTL formulae
beyond those necessary to distinguish finite from infinite
interpretations. We generate plans by compiling LTL temporally
extended goals into problem instances described in
the Planning Domain Definition Language that are solved by
a state-of-the-art fully observable non-deterministic planner.
We propose several different compilations based on translations
of LTL to alternating or non-deterministic (B¨uchi) automata,
and evaluate various properties of the competing approaches.
We address a diverse spectrum of LTL planning
problems that, to this point, had not been solvable using AI
planning techniques, and do so in a manner that demonstrates
highly competitive performance.}
}




@incollection{brown2016500,
  author      = {Christian Muise},
  title       = {A Flow Shop Scheduler},
  editor      = {Brown, Amy and Dibernardo, Michael},
  booktitle   = {500 Lines or Less},
  publisher   = {Lulu.com},
  year        = 2016,
  %pages       = {},
  url         = {http://aosabook.org/en/500L/a-flow-shop-scheduler.html},
  chapter     = 9,
}



@article{coin-journal-teamwork,
  title = {Towards Team Formation via Automated Planning},
  author = {Christian Muise and Frank Dignum and Paolo Felli and Tim Miller and Adrian R. Pearce and Liz Sonenberg},
  journal = {Lecture Notes in Computer Science},
  volume = {9628},
  publisher = {Springer},
  pages = {282-299},
  year = {2016},
  note = {Special Issue on Coordination, Organizations, Institutions, and Norms in Agent Systems XI},
  keywords = {multi-agent planning, epistemic reasoning},
  url = {http://link.springer.com/chapter/10.1007%2F978-3-319-42691-4_16},
  abstract={Cooperative problem solving involves four key phases: (1) finding potential members to form a team, (2) forming the team, (3) formulating a plan for the team, and (4) executing the plan. We extend recent work on multi-agent epistemic planning and apply it to the problem of team formation in a blocksworld scenario. We provide an encoding of the first three phases of team formation from the perspective of an initiator, and show how automated planning efficiently yields conditional plans that guarantee certain collective intentions will be achieved. The expressiveness of the epistemic planning formalism, which supports modelling with the nested beliefs of agents, opens the prospect of broad applicability to the operationalisation of collective intention.}
}



@inproceedings{mombourquette-knowpros16-bf,
  title = {Belief State Estimation for Planning via Approximate Logical Filtering and Smoothing},
  author = {Brent Mombourquette and Christian Muise and Sheila A. McIlraith},
  booktitle = {Workshop on Knowledge-based techniques for problem solving and reasoning ({KnowProS}'16)},
  year = {2016},
  url = {http://ceur-ws.org/Vol-1648/paper8.pdf},
  abstract={State estimation is the task of estimating the state of a partially observable dynamical system given a sequence of executed actions and observations. In logical settings, state estimation can be realized via logical filtering. Unfortunately such filtering, though exact, can be intractable. To this end, we propose logical smoothing, a form of backwards reasoning that works in concert with logical filtering to refine past beliefs in light of new observations. We characterize the notion of logical smoothing together with an algorithm for backwards-forwards state estimation. We prove properties of our algorithms, and experimentally demonstrate their behaviour. Smoothing together with backwards-forwards reasoning are important techniques for reasoning about partially observable dynamical systems, introducing the logical analogue of effective techniques from control theory and dynamic programming}
}



@inproceedings{camacho-knowpros16-ltl,
  title = {Non-Deterministic Planning with Temporally Extended Goals: Completing the story for finite and infinite LTL},
  author = {Alberto Camacho and Eleni Triantafillou and Christian Muise and Jorge Baier and Sheila A. McIlraith},
  booktitle = {Workshop on Knowledge-based techniques for problem solving and reasoning ({KnowProS}'16)},
  year = {2016},
  url = {http://ceur-ws.org/Vol-1648/paper10.pdf},
  abstract={Temporally extended goals are critical to the specification of a diversity of real-world planning problems. Here we examine the problem of planning with temporally extended goals over both finite and infinite traces where actions can be non-deterministic, and where temporally extended goals are specified in linear temporal logic (LTL). Unlike existing LTL planners, we place no restrictionson our LTL formulae beyond those necessary to distinguish finite from infinite trace interpretations. We realize our planner by compiling temporally extended goals, represented in LTL, into Planning Domain Definition Language problem instances, and exploiting a state-of-the-art fully observable non-deterministic planner to compute solutions. The resulting planner is sound and complete. Our approach exploits the correspondence between LTL and automata. We propose several different compilations based on translations of LTL to (Buchi) alternating or non-deterministic finite state automata, and evaluate various properties of the competing approaches. We address a diverse spectrum of LTL planning problems that, to this point, had not been solvable using AI planning techniques. We do so while demonstrating competitive performance relative to the state of the art in LTL planning.}
}



@article{jair-popgen,
  author    = {Christian Muise and J. Christopher Beck and Sheila A. McIlraith},
  title     = {Optimal Partial-Order Plan Relaxation via MaxSAT},
  journal   = {Journal of Artificial Intelligence Research},
  year      = {2016},
  url       = {http://www.jair.org/media/5128/live-5128-9534-jair.pdf},
  abstract  = {Partial-order plans (POPs) are attractive because of their least-commitment nature, which provides enhanced plan flexibility at execution time relative to sequential plans. Current research on automated plan generation focuses on producing sequential plans, despite the appeal of POPs. In this paper we examine POP generation by relaxing or modifying the action orderings of a sequential plan to optimize for plan criteria that promote flexibility. Our approach relies on a novel partial weighted MaxSAT encoding of a sequential plan that supports the minimization of deordering or reordering of actions. We further demonstrate how to remove redundant actions from the plan. Our partial weighted MaxSAT encoding allows us to compute a POP from a sequential plan effectively. We compare the efficiency of our approach to previous methods for POP generation via sequential-plan relaxation. Our results show that while an existing heuristic approach consistently produces the optimal deordering of a sequential plan, our approach has greater flexibility when we consider reordering the actions in the plan while also providing a guarantee of optimality. We also investigate and confirm the accuracy of the standard $\flex$ metric typically used to predict the true flexibility of a POP as measured by the number of linearizations it represents.}
}



@inproceedings{muise-icaps16demo-pd,
  title = {{Planning.Domains}},
  author = {Christian Muise},
  booktitle = {The 26th International Conference on Automated Planning and Scheduling - Demonstrations},
  year = {2016},
  url = {http://www.haz.ca/papers/planning-domains-icaps16.pdf},
  abstract = {Commonly used resources for the field of automated planning, such as benchmarks, problem generators, etc., are widespread over the internet. With planning.domains, we aim to (a) collect these resources in a central location; and (b) enable creative possibilities through a consistent interface to the larger planning community. In this demo, we focus on the three main pillars of planning.domains: (1) api.planning.domains – a programmatic interface to all existing planning problems; (2) solver.planning.domains – an open (and extendable) interface to planning-in-the-cloud; and (3) editor.planning.domains - a fully featured editor for planning domains.}
}



@inproceedings{camacho-hsdip16-ltl,
  title = {LTL Synthesis for Non-Deterministic Systems on Finite and Infinite Traces},
  author = {Alberto Camacho and Eleni Triantafillou and Christian Muise and Jorge Baier and Sheila A. McIlraith},
  booktitle = {Workshop on Heuristic Search and Domain Independent Planning ({HSDIP}'16)},
  year = {2016},
  url = {http://www.haz.ca/papers/camacho-hsdip16-ltl.pdf},
  keywords = {non-deterministic planning, plan generation},
  abstract={Temporally extended goals are critical to the specification of a diversity of real-world planning problems. Here we examine the problem of planning with temporally extended goals over both finite and infinite traces where actions can be non-deterministic, and where temporally extended goals are specified in linear temporal logic (LTL). Unlike existing LTL planners, we place no restrictions on our LTL formulae beyond those necessary to distinguish finite from infinite trace interpretations. We realize our planner by translating LTL goals into either (Büchi) alternating or non-deterministic finite state automata, and exploiting a state-of-the-art fully observable non-deterministic planner to compute solutions. The resulting planner is sound and complete. Our system addresses a diverse spectrum of LTL planning problems that to this point had not been solvable using AI planning techniques. We do so while demonstrating competitive performance relative to the state of the art in LTL planning.}
}



@inproceedings{miller-ijcai16,
  title = {Belief Update for Proper Epistemic Knowledge Bases},
  author = {Tim Miller and Christian Muise},
  booktitle = {International Joint Conference On Artificial Intelligence},
  year = {2016},
  url = {http://www.haz.ca/papers/miller-ijcai-16.pdf},
  keywords = {multi-agent planning, epistemic reasoning},
  abstract={Reasoning about the nested beliefs of others is important in many multi-agent scenarios. While epistemic and doxastic logics lay a solid groundwork to approach such reasoning, the computational complexity of these logics is often too high for many tasks. Proper Epistemic Knowledge Bases (PEKBs) enforce two syntactic restrictions on formulae to obtain efficient querying: both disjunction and infinitely long nestings of modal operators are not permitted. PEKBs can be compiled, in exponential time, to a prime implicate formula that can be queried in polynomial time, while more recently, it was shown that consistent PEKBs had certain logical properties that meant this compilation was unnecessary, while still retaining polynomial-time querying. In this paper, we present a belief update mechanism for PEKBs that ensures the knowledge base remains consistent when new beliefs are added. This is achieved by first erasing any formulae that contradict these new beliefs. We show that this update mechanism can be computed in polynomial time, and we assess it against the well-known KM postulates for belief update.}
}



@inproceedings{muise-ijcai16-mapasfond,
  title = {Planning for a Single Agent in a Multi-Agent Environment Using FOND},
  author = {Christian Muise and Paolo Felli and Tim Miller and Adrian R. Pearce and Liz Sonenberg},
  booktitle = {International Joint Conference On Artificial Intelligence},
  year = {2016},
  url = {http://www.haz.ca/papers/muise-ijcai16.pdf},
  keywords = {non-deterministic planning, multi-agent planning},
  abstract={Single-agent planning in a multi-agent environment is challenging because the actions of other agents can affect our ability to achieve a goal. From a given agent's perspective, actions of others can be viewed as non-deterministic outcomes of that agent's actions. While simple conceptually, this interpretation of planning in a multi-agent environment as non-deterministic planning remains challenging, not only due to the non-determinism resulting from others' actions, but because it is not clear how to compactly model the possible actions of others in the environment. In this paper, we cast the problem of planning in a multi-agent environment as one of Fully-Observable Non-Deterministic (FOND) planning. We extend a non-deterministic planner to plan in a multi-agent setting, allowing non-deterministic planning technology to solve a new class of planning problems. To improve the efficiency in domains too large for solving optimally, we propose a technique to use the goals and possible actions of other agents to focus the search on a set of plausible actions. We evaluate our approach on existing and new multi-agent benchmarks, demonstrating that modelling the other agents' goals improves the quality of the resulting solutions.}
}



@inproceedings{lipovetzky-icaps16-trapper,
  title = {Traps, Invariants, and Dead-Ends},
  author = {Nir Lipovetzky and Christian Muise and Hector Geffner},
  booktitle = {The 26th International Conference on Automated Planning and Scheduling},
  year = {2016},
  url = {http://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/13190},
  keywords = {deadends, plan generation},
  abstract={We consider the problem of deriving formulas that capture traps, invariants, and dead-ends in classical planning through polynomial forms of preprocessing. An invariant is a formula that is true in the initial state and in all reachable states. A trap is a conditional invariant: once a state is reached that makes the trap true, all the states that are reachable from it will satisfy the trap formula as well. Finally, dead-ends are formulas that are satisfied in states that make the goal unreachable. We introduce a preprocessing algorithm that computes traps in k-DNF form that is exponential in the k parameter, and show how the algorithm can be used to precompute invariants and dead-ends. We report also preliminary tests that illustrate the effectiveness of the preprocessing algorithm for identifying dead-end states, and compare it with the identification that follows from the use of the $h^1$ and $h^2$ heuristics that cannot be preprocessed, and must be computed at run time.}
}



@inproceedings{camacho-icaps16-probprp,
  title = {From FOND to Robust Probabilistic Planning: Computing compact policies that bypass avoidable deadends},
  author = {Alberto Camacho and Christian Muise and Sheila A. McIlraith},
  booktitle = {The 26th International Conference on Automated Planning and Scheduling},
  year = {2016},
  url = {http://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/13188},
  keywords = {non-deterministic planning, probabilistic planning, plan generation},
  abstract={We address the class of probabilistic planning problems where the objective is to maximize the probability of reaching a prescribed goal. The complexity of probabilistic planning problems makes it difficult to compute high quality solutions for large instances, and existing algorithms either do not scale, or do so at the expense of the solution quality. We leverage core similarities between probabilistic and fully observable non-deterministic (FOND) planning to construct a sound, offline probabilistic planner, ProbPRP, that exploits algorithmic advances from state-of-the-art FOND planner, PRP, to compute compact policies that are guaranteed to bypass avoidable deadends. We evaluate ProbPRP on a selection of benchmarks used in past probabilistic planning competitions. The results show that ProbPRP, in many cases, outperforms the state of the art, computing substantially more robust policies and at times doing so orders of magnitude faster.}
}



@inproceedings{miller-aaai-16,
  author = {Tim Miller and Christian Muise and Paolo Felli and Adrian R. Pearce and Liz Sonenberg},
  year = {2016},
  booktitle = {The 30th AAAI Conference on Artificial Intelligence},
  keywords = {multi-agent planning, epistemic reasoning},
  url = {http://www.haz.ca/papers/miller-aaai-16.pdf},
  title = {'Knowing Whether' in Proper Epistemic Knowledge Bases},
  abstract = {Proper epistemic knowledge bases (PEKBs) are syntactic knowledge bases that use multi-agent epistemic logic to represent nested multi-agent knowledge and belief. PEKBs have certain syntactic restrictions that lead to desirable computational properties; primarily, a PEKB is a conjunction of modal literals, and therefore contains no disjunction. Sound entailment can be checked in polynomial time, and is complete for a large set of arbitrary formulae in logics Kn and KDn. In this paper, we extend PEKBs to deal with a restricted form of disjunction: ‘knowing whether’. An agent i knows whether ϕ iff agent i knows ϕ or knows ¬ϕ; that is, iϕ ∨ i¬ϕ. In our experience, the ability to represent that an agent knows whether something holds is useful in many multi-agent domains. We represent knowing whether with a modal operator, ∆i, and present sound polynomial-time entailment algorithms on PEKBs with ∆i in Kn and KDn, but which are complete for a smaller class of queries than standard PEKBs.}
}



@inproceedings{sonenberg-cashri16,
  author = {Liz Sonenberg and Tim Miller and Adrian Pearce and Paolo Felli and Christian Muise and Frank Dignum},
  title = {Social planning for social HRI},
  booktitle = {2nd Workshop on Cognitive Architectures for Social Human-Robot Interaction},
  year = {2016},
  keywords = {human-agent collaboration},
  url = {http://arxiv.org/pdf/1602.06483v1.pdf}
}



@inproceedings{muise-beyondnp16-dsharp,
author = {Muise, Christian and McIlraith, Sheila A. and Beck, J. Christopher and Hsu, Eric},
booktitle = {AAAI-16 Workshop on Beyond NP},
title = {{DSHARP: Fast d-DNNF Compilation with sharpSAT (Amended Version)}},
keywords = {sat, knowledge compilation},
url = {http://haz.ca/dsharp-related.html},
year = {2016}
}



@inproceedings{Aziz_CSM_15,
  author = {Rehan Abdul Aziz and Geoffrey Chu and Christian Muise and Peter Stuckey},
  year = {2015},
  booktitle = {International Conference on Theory and Applications of Satisfiability Testing},
  address = "Austin, {USA}",
  keywords = {sat, knowledge compilation},
  title = {Projected Model Counting},
  url = {http://arxiv.org/pdf/1507.07648v1.pdf},
  abstract = {Model counting is the task of computing the number of assignments to variables V that satisfy a given propositional theory F. The model counting problem is denoted as #SAT. Model counting is an essential tool in probabilistic reasoning. In this paper, we introduce the problem of model counting projected on a subset of original variables that we call priority variables P ⊆ V. The task is to compute the number of assignments to P such that there exists an extension to non-priority variables V \ P that satisfies F. We denote this as #∃SAT. Projected model counting arises when some parts of the model are irrelevant to the counts, in particular when we require additional variables to model the problem we are counting in SAT. We discuss three different approaches to #∃SAT (two of which are novel), and compare their performance on different benchmark problems.}
}



@inproceedings{muise-codmap15,
  title = {{MAP-LAPKT}: Omnipotent Multi-Agent Planning via Compilation to Classical Planning},
  author = {Christian Muise and Nir Lipovetzky and Miquel Ramirez},
  booktitle = {Competition of Distributed and Multiagent Planners},
  year = {2015},
  keywords = {multi-agent planning},
  url = {http://www.haz.ca/papers/muise_CoDMAP15.pdf}
}



@inproceedings{muise-wipc15,
  title = {Unplannability IPC Track},
  author = {Christian Muise and Nir Lipovetzky},
  booktitle = {Workshop on the International Planning Competition},
  year = {2015},
  keywords = {unsolvability,deadends},
  url = {http://www.haz.ca/papers/muise_WIPC15.pdf},
  abstract = {The majority of research in the field of automated planning focuses on the synthesis of plans for problems that are solvable. We propose an IPC track to focus on the important and understudied area of unplannibility: proving that a planning problem is unsolvable. We will focus on classical planning problems, as methods for determining whether or not unplannability can have wider applications for classical planning problems (e.g., recognizing and avoiding deadends in the state space) as well as solving planning problems with uncertainty (e.g., identifying when a deterministic approximation of the problem is unsolvable). The unplannability track follows similar contests in other fields; for example, the UNSAT track for the field of Boolean Satisfiability. In a similar vein, we hope that the introduction of an unplannability track will foster new innovation for techniques dedicated to identifying planning problems that cannot be solved.}
}



@inproceedings{muise-coin15-teamwork,
  title = {Towards Team Formation via Automated Planning},
  author = {Christian Muise and Frank Dignum and Paolo Felli and Tim Miller and Adrian R. Pearce and Liz Sonenberg},
  booktitle = {International Workshop on Coordination, Organisation, Institutions and Norms in Multi-Agent Systems},
  year = {2015},
  keywords = {multi-agent planning, epistemic reasoning},
  url = {http://www.haz.ca/papers/muise-coinATijcai-15.pdf},
  abstract={Cooperative problem solving involves four key phases: (1) finding potential members to form a team, (2) forming the team, (3) formulating a plan for the team, and (4) executing the plan. We extend recent work on multi-agent epistemic planning and apply it to the problem of team formation in a blocksworld scenario. We provide an encoding of the first three phases of team formation from the perspective of an initiator, and show how automated planning efficiently yields conditional plans that guarantee certain collective intentions will be achieved. The expressiveness of the epistemic planning formalism, which supports modelling with the nested beliefs of agents, opens the prospect of broad applicability to the operationalisation of collective intention.}
}




@inproceedings{camacho-hsdip15-probprp,
  title = {From FOND to Probabilistic Planning: Guiding search for quality policies},
  author = {Alberto Camacho and Christian Muise and Akshay Ganeshen and Sheila A. McIlraith},
  booktitle = {Workshop on Heuristic Search and Domain Independent Planning ({HSDIP}'15)},
  year = {2015},
  keywords = {non-deterministic planning, probabilistic planning, plan generation},
  url={http://www.haz.ca/papers/camacho-hsdip15-probprp.pdf},
  abstract={We address the class of probabilistic planning problems where the objective is to maximize the probability of reaching a prescribed goal (MAXPROB). State-of-the-art probabilistic planners, and in particular MAXPROB planners, offer few guarantees with respect to the quality or optimality of the solutions that they find. The complexity of MAXPROB problems makes it difficult to compute high quality solutions for big problems, and existing algorithms either do not scale well, or provide poor quality solutions. We exploit core similarities between probabilistic and fully observable non-deterministic (FOND) planning models to extend the state-of-the-art FOND planner, PRP, to be a sound and sometimes complete MAXPROB solver that is guaranteed to sidestep avoidable deadends. We evaluate our planner, ProbPRP, on a selection of benchmarks used in past probabilistic planning competitions. The results show that ProbPRP outperforms previous state-of-the-art algorithms for solving MAXPROB, and computes substantially more robust policies, at times doing so orders of magnitude faster.}
}



@inproceedings{muise-dmap15-pdkbplanning,
  title = {Planning Over Multi-Agent Epistemic States: A Classical Planning Approach (Amended Version)},
  author = {Christian Muise and Vaishak Belle and Paolo Felli and Sheila A. McIlraith and Tim Miller and Adrian R. Pearce and Liz Sonenberg},
  booktitle = {Workshop on Distributed and Multi-Agent Planning ({DMAP}'15)},
  year = {2015},
  keywords = {multi-agent planning, epistemic reasoning},
  url={http://www.haz.ca/papers/muise-dmap15-pdkbplanning.pdf},
  abstract={Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology. Our approach represents an important first step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents.}
}



@inproceedings{muise-dmap15-mapasfond,
  title = {Leveraging FOND Planning Technology to Solve Multi-Agent Planning Problems},
  author = {Christian Muise and Paolo Felli and Tim Miller and Adrian R. Pearce and Liz Sonenberg},
  booktitle = {Workshop on Distributed and Multi-Agent Planning ({DMAP}'15)},
  year = {2015},
  keywords = {non-deterministic planning, multi-agent planning},
  url={http://www.haz.ca/papers/muise-dmap15-mapasfond.pdf},
  abstract={Single-agent planning in a multi-agent environment is challenging because the actions of other agents can affect our ability to achieve a goal. From a given agent's perspective, actions of others can be viewed as non-deterministic outcomes of that agent's actions. While simple conceptually, this interpretation of planning in a multi-agent environment as non-deterministic planning is challenging due to the non-determinism resulting from others' actions, and because it is not clear how to compactly model the possible actions of others in the environment. In this paper, we cast the problem of planning in a multi-agent environment as one of Fully-Observable Non-Deterministic (FOND) planning. We extend a non-deterministic planner to plan in a multi-agent setting, given the goals and possible actions of other agents. We use the other agents' goals to reduce their set of possible actions to a set of plausible actions, allowing non-deterministic planning technology to solve a new class of planning problems in first-person multi-agent environments. We demonstrate our approach on new and existing multi-agent benchmarks, demonstrating that modelling the other agents' goals reduces complexity.}
}



@inproceedings{felli-ijcai-2015,
  title = {Computing Social Behaviours Using Agent Models},
  author = {Felli, Paolo and Miller, Tim and Muise, Christian and Pearce, Adrian R. and Sonenberg, Liz},
  booktitle = {International Joint Conference on Artificial Intelligence, {IJCAI} 2015},
  year = {2015},
  keywords = {human-agent collaboration},
  url={http://www.haz.ca/papers/felli-ijcai15.pdf},
  abstract = {Agents can be thought of as following a social behaviour, depending on the context in which they are interacting. We devise a computationally grounded mechanism to represent and reason about others in social terms, reflecting the local perspective of an agent (first-person view), to support both stereotypical and empathetic reasoning. We use a hierarchy of agent models to discriminate which behaviours of others are plausible, and decide which behaviour for ourselves is socially acceptable, i.e. conforms to the social context. To this aim, we investigate the implications of considering agents capable of various degrees of Theory of Mind, and discuss a scenario showing how this affects behaviour.}
}



@inproceedings{muise-aamas-15,
  abstract={Reasoning about the nested beliefs or knowledge of other agents is essential for many collaborative and competitive tasks. However, reasoning with nested belief (for example through epistemic logics) is computationally expensive. Proper Epistemic Knowledge Bases (PEKBs) address this by enforcing syntactic restrictions on the knowledge base. By compiling a PEKB and query formula into a specific normal form, entailment can be checked in polynomial time, which is sound and complete for the epistemic logic Kn. The downside is that the complexity of compiling into the normal form is exponential in time and space. In this work, we extend PEKBs to handle belief in the logic of Kd. We show that this simplifies the complexity of the required reasoning, and importantly, achieves polynomial entailment checking without first having to compile the PEKB into a normal form. Also, we present an alternative approach that calculates the closure of a PEKB, which is exponential in the maximum depth of nested belief, but for which entailment checking is constant on average.},
  title={Efficient Reasoning With Consistent Proper Epistemic Knowledge Bases},
  author={Christian Muise and Tim Miller and Paolo Felli and Adrian R. Pearce and Liz Sonenberg},
  booktitle={The International Conference on Autonomous Agents and Multiagent Systems},
  year={2015},
  keywords = {multi-agent planning, epistemic reasoning},
  url={http://www.haz.ca/papers/muise-aamas-15.pdf}
}



@inproceedings{aziz-aaai-15,
  abstract={Model counting is the problem of computing the number of models that satisfy a given propositional theory. It has recently been applied to solving inference tasks in probabilistic logic programming, where the goal is to compute the probability of given queries being true provided a set of mutually independent random variables, a model (a logic program) and some evidence. The core of solving this inference task involves translating the logic program to a propositional theory and using a model counter. In this paper, we show that for some problems that involve inductive definitions like reachability in a graph, the translation of logic programs to SAT can be expensive for the purpose of solving inference tasks. For such problems, direct implementation of stable model semantics allows for more efficient solving. We present two implementation techniques, based on unfounded set detection, that extend a propositional model counter to a stable model counter. Our experiments show that for particular problems, our approach can outperform a state-of-the-art probabilistic logic programming solver by several orders of magnitude in terms of running time and space requirements, and can solve instances of significantly larger sizes on which the current solver runs out of time or memory.},
  title={Stable model counting and its application in probabilistic logic programming},
  author={Rehan Abdul Aziz and Geoffrey Chu and Christian Muise and Peter Stuckey},
  booktitle={The 29th AAAI Conference on Artificial Intelligence},
  keywords = {sat, knowledge compilation},
  year={2015},
  url={http://arxiv.org/pdf/1411.5410v1.pdf}
}



@inproceedings{muise-aaai-15,
  abstract={Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology. Our approach represents an important first step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents.},
  title={Planning Over Multi-Agent Epistemic States: A Classical Planning Approach},
  author={Christian Muise and Vaishak Belle and Paolo Felli and Sheila A. McIlraith and Tim Miller and Adrian R. Pearce and Liz Sonenberg},
  booktitle={The 29th AAAI Conference on Artificial Intelligence},
  year={2015},
  keywords = {multi-agent planning, epistemic reasoning},
  url={http://www.haz.ca/papers/muise-aaai-15.pdf}
}



@inproceedings{felli-icsr-2014,
  abstract={With a view to supporting expressive, but tractable, collaborative interactions between humans and agents, we propose an approach for representing heterogeneous agent models, i.e., with potentially diverse mental abilities and holding stereotypical characteristics as members of a social reference group. We build a computationally grounded mechanism for progressing their beliefs about others' beliefs, supporting stereotypical as well as empathic reasoning. We comment on how this approach can be used to build finite-state games, restricting the analysis of possibly large-scale problems by focusing only on the set of plausible evolutions.},
  title={Artificial social reasoning: computational mechanisms for reasoning about others},
  author={Felli, Paolo and Miller, Tim and Muise, Christian and Pearce, Adrian R. and Sonenberg, Liz},
  booktitle={The International Conference on Social Robotics},
  keywords = {human-agent collaboration},
  year={2014},
  url={http://agentlab.cis.unimelb.edu.au/papers/felli-icsr-2014.pdf}
}



@inproceedings{miller-aaai-symposium-2014,
  title={Foundations of Human-Agent Collaboration: Situation-Relevant Information Sharing},
  author={Miller, Tim and Pearce, Adrian R. and Sonenberg, Liz and Dignum, Frank and Felli, Paolo and Muise, Christian},
  booktitle={The AAAI 2014 Fall Symposium on AI for Human-Robot Interaction},
  year={2014},
  keywords = {human-agent collaboration},
  url={http://agentlab.cis.unimelb.edu.au/papers/miller-aaai-symposium-2014.pdf},
  note = {Extended Abstract}
}



@inproceedings{lipovetzky2014width,
  title={Width and Inference Based Planners: SIW, BFS (f), and PROBE},
  author={Lipovetzky, Nir and Ramirez, Miquel and Muise, Christian and Geffner, Hector},
  url={http://people.eng.unimelb.edu.au/nlipovetzky/papers/ipc8_siw_bfs_probe.pdf},
  booktitle={International Planning Competition Booklet (IPC-8)},
  keywords = {plan generation},
  year={2014}
}



@inproceedings{muise-aaai-14,
  abstract={Planning with sensing actions under partial observability is
a computationally challenging problem that is fundamental
to the realization of AI tasks in areas as diverse as robotics,
game playing, and diagnostic problem solving. Recent work
on generating plans for partially observable domains has advocated
for online planning, claiming that offline plans are
often too large to generate. Here we push the envelope on
this challenging problem, proposing a technique for generating
conditional (aka contingent) plans offline. The key to our
planner’s success is the reliance on state-of-the-art techniques
for fully observable non-deterministic (FOND) planning. In
particular, we use an existing compilation for converting a
planning problem under partial observability and sensing to
a FOND planning problem. With a modified FOND planner
in hand, we are able to scale beyond previous techniques for
generating conditional plans with solutions that are orders of
magnitude smaller than previously possible in some domains.},
  title={Computing Contingent Plans via Fully Observable Non-Deterministic Planning},
  author={Muise, Christian and Belle, Vaishak and McIlraith, Sheila A.},
  booktitle={The 28th AAAI Conference on Artificial Intelligence},
  year={2014},
  keywords = {non-deterministic planning, plan generation, partial observability, sensing},
  url={http://www.haz.ca/papers/muise-aaai-14.pdf}
}



@inproceedings{muise-icaps-14,
  abstract={Recent advances in fully observable non-deterministic
(FOND) planning have enabled new techniques for various
applications, such as behaviour composition, among others.
One key limitation of modern FOND planners is their lack
of native support for conditional effects. In this paper we
describe an extension to PRP, the current state of the art in
FOND planning, that supports the generation of policies for
domains with conditional effects and non-determinism. We
present core modifications to the PRP planner for this enhanced
functionality without sacrificing soundness and completeness.
Additionally, we demonstrate the planner’s capabilities on a
variety of benchmarks that include actions with
both conditional effects and non-deterministic outcomes. The
resulting planner opens the door to models of greater
expressivity, and does so without affecting PRP’s efficiency.},
  title={Non-Deterministic Planning With Conditional Effects},
  author={Muise, Christian and McIlraith, Sheila A. and Belle, Vaishak},
  booktitle={The 24th International Conference on Automated Planning and Scheduling},
  year={2014},
  keywords = {non-deterministic planning, plan generation},
  url={http://www.haz.ca/papers/muise-icaps-14.pdf}
}




@inproceedings{klebanovsat,
  title={{SAT-based Analysis and Quantification of Information Flow in Programs}},
  author={Klebanov, Vladimir and Manthey, Norbert and Muise, Christian},
  booktitle={10th International Conference on Quantitative Evaluation of SysTems (QEST 2013)},
  year={2013},
  pages={177--192},
  keywords = {sat, knowledge compilation},
  url={http://formal.iti.kit.edu/~klebanov/pubs/qest2013.pdf}
}



@inproceedings{muise-ijcai-13,
  title={{Flexible Execution of Partial Order Plans With Temporal Constraints}},
  author={Muise, Christian and Beck, J. Christopher and McIlraith, Sheila A.},
  booktitle={International Joint Conference On Artificial Intelligence},
  year={2013},
  pages={2328--2335},
  keywords = {plan execution,temporal},
  url={http://ijcai.org/papers13/Papers/IJCAI13-343.pdf}
}



@inproceedings{muise-icaps12-long,
abstract = {We address the problem of computing a policy for fully ob- servable non-deterministic (FOND) planning problems. By focusing on the relevant aspects of the state of the world, we introduce a series of improvements to the previous state of the art and extend the applicability of our planner, PRP, to work in an online setting. The use of state relevance allows our policy to be exponentiallymore succinct in representing a solution to a FOND problem for some domains. Through the introduction of newtechniques for avoiding deadends and de- termining sufficient validity conditions, PRP has the potential to compute a policy up to several orders of magnitude faster than previous approaches. We also find dramatic improve- ments over the state of the art in online replanning when we treat suitable probabilistic domains as FOND domains.},
author = {Muise, Christian and McIlraith, Sheila A. and Beck, J. Christopher},
booktitle = {The 22nd International Conference on Automated Planning and Scheduling},
series = {The 22nd International Conference on Automated Planning and Scheduling},
title = {{Improved Non-deterministic Planning by Exploiting State Relevance}},
keywords = {plan generation,non-deterministic planning},
year = {2012},
url = {http://www.haz.ca/papers/muise-icaps2012-fond.pdf}
}



@inproceedings{muise2012optimally,
  title={{Optimally Relaxing Partial-order Plans With MaxSAT}},
  author={Muise, Christian and McIlratih, Sheila A. and Beck, J. Christopher},
  booktitle={22nd International Conference on Automated Planning and Scheduling},
  url={http://tidel.mie.utoronto.ca/pubs/muise_MaxSATICAPS2012.pdf},
  keywords = {sat,plan optimization},
  year={2012}
}



@inproceedings{Muise2012,
author = {Muise, Christian and McIlraith, Sheila A. and Beck, J. Christopher and Hsu, Eric},
booktitle = {Canadian Conference on Artificial Intelligence},
title = {{DSHARP: Fast d-DNNF Compilation with sharpSAT}},
keywords = {sat, knowledge compilation},
url = {http://tidel.mie.utoronto.ca/pubs/MuiseDSHARP_AI2012.pdf},
year = {2012}
}



@inproceedings{de2012supervising,
  title={{On supervising agents in situation-determined ConGolog}},
  author={De Giacomo, Giuseppe and Lesp{\'e}rance, Yves and Muise, Christian},
  booktitle={Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems-Volume 2},
  pages={1031--1038},
  year={2012},
  keywords = {sitcalc},
  organization={International Foundation for Autonomous Agents and Multiagent Systems},
  url={http://www.cse.yorku.ca/~lesperan/papers/AAMAS12.pdf}
}



@inproceedings{muise-ijcai11,
abstract = {Partial-order plans (POPs) have the capacity to compactly represent numerous distinct plan lin- earizations and as a consequence are inherently ro- bust. We exploit this robustness to do effective ex- ecutionmonitoring. We characterize the conditions underwhich a POP remains viable as the regression of the goal through the structure of a POP.We then develop a method for POP execution monitoring via a structured policy, expressed as an ordered al- gebraic decision diagram. The policy encompasses both state evaluation and action selection, enabling an agent to seamlessly switch between POP lin- earizations to accommodate unexpected changes during execution. We demonstrate the effective- ness of our approach by comparing it empirically and analytically to a standard technique for execu- tion monitoring of sequential plans. On standard benchmark planning domains, our approach is 2 to 17 times faster and up to 2.5 times more robust than comparable monitoring of a sequential plan. On POPs that have few ordering constraints among actions, our approach is significantly more robust, with the ability to continue executing in up to an exponential number of additional states.},
author = {Muise, Christian and McIlraith, Sheila A. and Beck, J. Christopher},
booktitle = {International Joint Conference On Artificial Intelligence},
series = {International Joint Conference On Artificial Intelligence},
title = {{Monitoring the Execution of Partial-Order Plans via Regression}},
year = {2011},
keywords = {plan execution},
url = {http://ijcai.org/papers11/Papers/IJCAI11-330.pdf}
}



@inproceedings{muise2011optimally,
  title={{Optimization of Partial-Order Plans via MaxSAT}},
  author={Muise, Christian and McIlratih, Sheila A. and Beck, J. Christopher},
  booktitle={Workshop on Constraint Satisfaction Techniques for Planning and Scheduling Problems (COPLAS'11)},
  year={2011},
  keywords = {sat,plan optimization},
  url={http://www.haz.ca/papers/coplas-muise-11.pdf}
}



@inproceedings{DeGiacomo2011,
abstract = {We investigate agent supervision, a form of customization, which constrains the actions of an agent so as to enforce certain desired behavioral specifications. This is done in a setting based on the Situation Calculus and a variant of the ConGolog programming language which allows for nondeterminism, but requires the remainder of a program after the execution of an action to be determined by the resulting situation. Such programs can be fully characterized by the set of action sequences that they generate. The main results are a characterization of the maximally permissive supervisor that minimally constrains the agent so as to enforce the desired behavioral constraints when some agent actions are uncontrollable, and a sound and complete technique to execute the agent as constrained by such a supervisor.},
author = {{De Giacomo}, Giuseppe and Lesperance, Yves and Muise, Christian},
booktitle = {Nonmonotonic Reasoning, Action and Change},
title = {{Agent Supervision in Situation-Determined ConGolog}},
url = {http://ijcai-11.iiia.csic.es/files/proceedings/W4- NRAC11-Proceedings.pdf\#page=27},
keywords = {sitcalc},
year = {2011}
}



@inproceedings{mui-etal-wara10,
address = {Atlanta, GA, USA},
author = {Muise, Christian and McIlraith, Sheila A. and Beck, J. Christopher},
booktitle = {Workshop on Abstraction, Reformulation, and Approximation (AAAI-10)},
title = {{Fast d-DNNF Compilation with sharpSAT}},
url = {http://www.haz.ca/papers/wara-muise-10.pdf},
keywords = {sat, knowledge compilation},
year = {2010}
}



@inproceedings{Muise2009,
address = {Thessaloniki, Greece},
author = {Muise, Christian and McIlraith, Sheila A. and Baier, Jorge A. and Reimer, Michael},
booktitle = {19th International Conference on Automated Planning and Scheduling},
title = {{Exploiting N-gram Analysis to Predict Operator Sequences}},
url = {http://www.haz.ca/papers/mui-etal-icaps09.pdf},
year = {2009},
keywords = {learning},
}



@inproceedings{DBLP:conf/cp/HsuMBM08,
address = {Sydney, Australia},
author = {Hsu, Eric I and Muise, Christian and Beck, J. Christopher and McIlraith, Sheila A.},
booktitle = {Principles and Practice of Constraint Programming, 14th International Conference},
pages = {613--617},
keywords = {sat},
title = {{Probabilistically Estimating Backbones and Variable Bias: Experimental Overview}},
url = {http://www.haz.ca/papers/hsu-cp2008.pdf},
year = {2008}
}



@mastersthesis{muise-mastersthesis,
  author       = {Christian Muise},
  title        = {Exploiting Modern \#SAT-Solving Techniques to Generate Implicants},
  school       = {University of Toronto},
  year         = 2009,
  keywords     = {thesis},
  url          = {http://www.haz.ca/papers/Muise-Masters-Thesis.pdf},
  abstract     = {An implicant of a propositional boolean formula is a conjunction of propositions that entails a the formula. Implicants play an important role for many tasks in AI such as knowledge compilation, circuit minimization, and diagnosis. There are many classes of implicants that are of interest including prime, irredundant, and orthogonal implicants. In general, implicants are hard to compute because the size of both the intermediate and/or final results can be exponentially large. In this work we focus on how advances in modern #SAT solvers can be exploited to effectively compute a complete set of orthogonal implicants. We develop an orthogonal implicant compiler and demonstrate its potential through experimental evaluation. Preliminary results indicate that this approach is effective at generating the orthogonal implicants of a propositional theory represented in Conjunctive Normal Form.}
}



@techreport{muise-depth,
  title={Plan Dispatchability: A Survey},
  author={Christian Muise},
  year={2011},
  institution={University of Toronto, Depth Examination Report},
  keywords={temporal},
  url={http://www.haz.ca/papers/muise-depth.pdf},
  url_slides={http://www.haz.ca/papers/muise-depth_slides.pdf}
}



@phdthesis{muise-phdthesis,
  author       = {Christian Muise},
  title        = {Exploiting Relevance to Improve Robustness and Flexibility in Plan Generation and Execution},
  school       = {University of Toronto},
  year         = 2014,
  keywords     = {thesis},
  url          = {http://www.haz.ca/papers/Muise-PhD-Thesis.pdf},
  abstract     = {Automated plan generation and execution is an essential component of most autonomous agents. An agent’s model of the world is often incomplete or incorrect, and its environment is typically noisy. To account for potential discrepancies between the agent’s model of the world and the true state of the world, the planning techniques and representations used should enable flexible and robust agent behaviour. The agent should react swiftly when unexpected changes occur to assess the impact of the discrepancy and to accommodate as necessary. In particular, the agent should avoid unnecessary replanning and recognize changes that are irrelevant for its plan to achieve the goal.

In this dissertation we address various aspects of the planning process including (1) how to synthesize a plan, (2) what a plan should constitute and how we should represent one, and (3) how to effectively execute a plan. We enable robust and flexible agent behaviour by exploiting the notion of relevance in each of the key planning areas. Intuitively, relevance characterizes what is important to consider as a sufficient condition for some property to hold. We apply relevance to the key areas of automated planning to achieve the following contributions: (1) increased flexibility of partial-order plans, (2) improved robustness of partial-order plan execution, (3) robust execution of temporally constrained plans, and (4) improved efficiency of plan generation with non-deterministic action effects.

To increase the flexibility of partial-order plans, we introduce an effective method for generating optimally relaxed partial-order plans. For the execution of a partial-order plan, we leverage regression to generalize an input plan, resulting in an execution monitoring framework that is far more robust than previous approaches. We incorporate the expressive power of temporal constraints and provide a means for monitoring the execution of a temporally constrained plan, building on our approach for executing a partial-order plan. Finally, we introduce a suite of techniques that leverage relevance to produce a state-of-the-art planner for domains with non-deterministic action effects. For each of these four areas, we investigate the theoretical properties surrounding our methods and empirically demonstrate their feasibility in comparison to the previous state of the art.}
}
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The service provides a queue manager to control a set of workers, which can easily be extended with one of several planners available in PLANUTILS. PaaS is designed to overcome the limitations of the existing online solver.planning.domains interface and widen the adoption of planning technology in education, research, and industry.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Planning as a service (PaaS) provides an extendable API to deploy planners online in local or cloud servers. The service provides a queue manager to control a set of workers, which can easily be extended with one of several planners available in PLANUTILS. PaaS is designed to overcome the limitations of the existing online solver.planning.domains interface and widen the adoption of planning technology in education, research, and industry.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"venezia-muise-plan4dialadialogueplanningframework-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://icaps23.icaps-conference.org/demos/papers/8139_paper.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'venezia-muise-plan4dialadialogueplanningframework-2023', 'https://icaps23.icaps-conference.org/demos/papers/8139_paper.pdf', event);\">\n    Plan4Dial: A Dialogue Planning Framework.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Venezia, R. D.;  and <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>International Conference on Automated Planning and Scheduling: System Demonstrations</i>.  2023.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://icaps23.icaps-conference.org/demos/papers/8139_paper.pdf\"\n     onclick=\"log_download('venezia-muise-plan4dialadialogueplanningframework-2023', 'https://icaps23.icaps-conference.org/demos/papers/8139_paper.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Plan4Dial: A Dialogue Planning Framework [pdf]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/venezia-muise-plan4dialadialogueplanningframework-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('venezia-muise-plan4dialadialogueplanningframework-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{devenezia-icapsdemo-2023,\n  title     = {Plan4Dial: A Dialogue Planning Framework},\n  author    = {Rebecca De Venezia and Christian Muise},\n  booktitle = {International Conference on Automated Planning and Scheduling: System Demonstrations},\n  year      = {2023},\n  url_paper = {https://icaps23.icaps-conference.org/demos/papers/8139_paper.pdf},\n  abstract  = {Dialogue agents have exploded in importance in recent years as businesses increasingly use chat-bots to serve their customer base. However, many of these dialogue systems rely on black-box language models that cannot be verified for predictability, making them a liability. One proposed solution is dialogue planning, which uses planning to generate a complete dialogue tree and allows for the verification of the agent’s actions. Despite the plethora of existing research in this space, there is no open and readily available modern framework for dialogue planning development. We propose Plan4Dial, an open-source system for creating dialogue planning chat-bots. Plan4Dial allows developers to declare complex chat-bots with ease by writing an intuitive YAML specification which the system converts to raw PDDL. Plan4Dial then calls a state-of-the-art planner to generate a dialogue tree which we execute with an extension of IBM’s dialogue plan executor, Hovor. We also created WIDGET, an embeddable web user interface for users to chat with their agents. Our work allows for the simple creation of complex but verifiable goal-oriented dialogue agents using planning technology.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Dialogue agents have exploded in importance in recent years as businesses increasingly use chat-bots to serve their customer base. However, many of these dialogue systems rely on black-box language models that cannot be verified for predictability, making them a liability. One proposed solution is dialogue planning, which uses planning to generate a complete dialogue tree and allows for the verification of the agent’s actions. Despite the plethora of existing research in this space, there is no open and readily available modern framework for dialogue planning development. We propose Plan4Dial, an open-source system for creating dialogue planning chat-bots. Plan4Dial allows developers to declare complex chat-bots with ease by writing an intuitive YAML specification which the system converts to raw PDDL. Plan4Dial then calls a state-of-the-art planner to generate a dialogue tree which we execute with an extension of IBM’s dialogue plan executor, Hovor. We also created WIDGET, an embeddable web user interface for users to chat with their agents. Our work allows for the simple creation of complex but verifiable goal-oriented dialogue agents using planning technology.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"morgan-muise-learningtorecognizereachablestatesfromvisualdomains-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://caiac.pubpub.org/pub/0ggeub8s/release/1\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'morgan-muise-learningtorecognizereachablestatesfromvisualdomains-2023', 'https://caiac.pubpub.org/pub/0ggeub8s/release/1', event);\">\n    Learning to Recognize Reachable States from Visual Domains.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Morgan, E.;  and <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>Canadian Conference on Artificial Intelligence</i>.  2023.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://caiac.pubpub.org/pub/0ggeub8s/release/1\"\n     onclick=\"log_download('morgan-muise-learningtorecognizereachablestatesfromvisualdomains-2023', 'https://caiac.pubpub.org/pub/0ggeub8s/release/1', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Learning to Recognize Reachable States from Visual Domains [link]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/morgan-muise-learningtorecognizereachablestatesfromvisualdomains-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('morgan-muise-learningtorecognizereachablestatesfromvisualdomains-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{morgan-cai-2023,\n  title     = {Learning to Recognize Reachable States from Visual Domains},\n  author    = {Ella Morgan and Christian Muise},\n  booktitle = {Canadian Conference on Artificial Intelligence},\n  year      = {2023},\n  url_paper = {https://caiac.pubpub.org/pub/0ggeub8s/release/1},\n  abstract  = {While planning models are symbolic and precise, the real world is noisy and unstructured. This work aims to bridge the gap between noise and structure by aligning visualizations of planning states to the underlying state space structure. Further, we do so in the presence of noise and augmentations that simulates a commonly overlooked property of real environments: several variations of semantically equivalent states. First, we create a dataset that visualizes states for several common planning domains; each state is generated in a way that introduces variability or noise. E.g., objects changing in location or appearance in a manner that preserves semantic meaning. First we train a contrastive learning model to predict the underlying states from the images. We then evaluate how we can align the predictions of a given sequence of visualized states with the problem’s reachable state space, taking advantage of the known structure to improve predictions. We compare two methods for doing so: a greedy approach and Viterbi’s algorithm, a well-established algorithm for observation decoding given a hidden Markov model. The results demonstrate that these alignment methods can correct errors in the model and significantly improve predictive accuracy.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  While planning models are symbolic and precise, the real world is noisy and unstructured. This work aims to bridge the gap between noise and structure by aligning visualizations of planning states to the underlying state space structure. Further, we do so in the presence of noise and augmentations that simulates a commonly overlooked property of real environments: several variations of semantically equivalent states. First, we create a dataset that visualizes states for several common planning domains; each state is generated in a way that introduces variability or noise. E.g., objects changing in location or appearance in a manner that preserves semantic meaning. First we train a contrastive learning model to predict the underlying states from the images. We then evaluate how we can align the predictions of a given sequence of visualized states with the problem’s reachable state space, taking advantage of the known structure to improve predictions. We compare two methods for doing so: a greedy approach and Viterbi’s algorithm, a well-established algorithm for observation decoding given a hidden Markov model. The results demonstrate that these alignment methods can correct errors in the model and significantly improve predictive accuracy.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"armstrong-muise-thegeneralizabilityoffondsolutionsinuncertainenvironments-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://mulab.ai/papers/2023-intex-armstrong.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'armstrong-muise-thegeneralizabilityoffondsolutionsinuncertainenvironments-2023', 'https://mulab.ai/papers/2023-intex-armstrong.pdf', event);\">\n    The Generalizability of FOND Solutions in Uncertain Environments.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Armstrong, V.;  and <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>Workshop on Integrated Acting, Planning and Execution</i>.  2023.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://mulab.ai/papers/2023-intex-armstrong.pdf\"\n     onclick=\"log_download('armstrong-muise-thegeneralizabilityoffondsolutionsinuncertainenvironments-2023', 'https://mulab.ai/papers/2023-intex-armstrong.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"The Generalizability of FOND Solutions in Uncertain Environments [pdf]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/armstrong-muise-thegeneralizabilityoffondsolutionsinuncertainenvironments-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('armstrong-muise-thegeneralizabilityoffondsolutionsinuncertainenvironments-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{armstrong-intex-2023,\n  title     = {The Generalizability of FOND Solutions in Uncertain Environments},\n  author    = {Victoria Armstrong and Christian Muise},\n  booktitle = {Workshop on Integrated Acting, Planning and Execution},\n  year      = {2023},\n  url_paper = {https://mulab.ai/papers/2023-intex-armstrong.pdf},\n  abstract  = {Logical regression has proven to be a powerful mechanism for computing compact solutions to non-deterministic planning problems. However, the impact on the generality of the generated solutions has thus far been largely unstudied. We analyze the compact solutions produced by a leading FOND planner, PRP, and develop a logical encoding that represents all possible states that the policy can handle. Through the use of a #-SAT solver, we count the number of models that satisfy the logical encoding (corresponding precisely to the states the policy is able to handle). We analyze the solution representation on seven standard FOND benchmarks and compare the generality of these policies to the reachable state space of the policy applied to the problem’s initial state. Our work can be seen as a generalization of similar studies for deterministic planning domains and clearly demonstrates the broad generalizability of these compact representations.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Logical regression has proven to be a powerful mechanism for computing compact solutions to non-deterministic planning problems. However, the impact on the generality of the generated solutions has thus far been largely unstudied. We analyze the compact solutions produced by a leading FOND planner, PRP, and develop a logical encoding that represents all possible states that the policy can handle. Through the use of a #-SAT solver, we count the number of models that satisfy the logical encoding (corresponding precisely to the states the policy is able to handle). We analyze the solution representation on seven standard FOND benchmarks and compare the generality of these policies to the reachable state space of the policy applied to the problem’s initial state. Our work can be seen as a generalization of similar studies for deterministic planning domains and clearly demonstrates the broad generalizability of these compact representations.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"petrov-muise-fromstatespacestosemigroupsleveragingalgebraicformalismforautomatedplanning-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://openreview.net/pdf?id=ocTyIiCroh\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'petrov-muise-fromstatespacestosemigroupsleveragingalgebraicformalismforautomatedplanning-2023', 'https://openreview.net/pdf?id=ocTyIiCroh', event);\">\n    From State Spaces to Semigroups: Leveraging Algebraic Formalism for Automated Planning.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Petrov, A.;  and <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>Workshop on Heuristics and Search for Domain-independent Planning</i>.  2023.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://openreview.net/pdf?id=ocTyIiCroh\"\n     onclick=\"log_download('petrov-muise-fromstatespacestosemigroupsleveragingalgebraicformalismforautomatedplanning-2023', 'https://openreview.net/pdf?id=ocTyIiCroh', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"From State Spaces to Semigroups: Leveraging Algebraic Formalism for Automated Planning [link]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/petrov-muise-fromstatespacestosemigroupsleveragingalgebraicformalismforautomatedplanning-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('petrov-muise-fromstatespacestosemigroupsleveragingalgebraicformalismforautomatedplanning-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{petrov-hsdip-2023,\n  title     = {From State Spaces to Semigroups: Leveraging Algebraic Formalism for Automated Planning},\n  author    = {Alice Petrov and Christian Muise},\n  booktitle = {Workshop on Heuristics and Search for Domain-independent Planning},\n  year      = {2023},\n  url_paper = {https://openreview.net/pdf?id=ocTyIiCroh},\n  abstract  = {This paper introduces an algebraic formalism linking transformation semigroups and the state transition systems induced by classical planning problems. We investigate some basic planning problems with interesting properties and establish fundamental characteristics of the corresponding semigroups, such as their ideals and Green’s relations. Furthermore, we leverage semigroup theory to propose new approaches to existing concepts in automated planning, including the identification of landmark actions and the study of dead ends. We demonstrate that algebraic results can be applied to facilitate an understanding of a planning problem’s state space and explore its solutions, thus verifying the relevance and effectiveness of such formal modeling.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This paper introduces an algebraic formalism linking transformation semigroups and the state transition systems induced by classical planning problems. We investigate some basic planning problems with interesting properties and establish fundamental characteristics of the corresponding semigroups, such as their ideals and Green’s relations. Furthermore, we leverage semigroup theory to propose new approaches to existing concepts in automated planning, including the identification of landmark actions and the study of dead ends. We demonstrate that algebraic results can be applied to facilitate an understanding of a planning problem’s state space and explore its solutions, thus verifying the relevance and effectiveness of such formal modeling.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"christen-eriksson-katz-muise-petrov-pommerening-seipp-sievers-etal-parisplanningalgorithmsforreconfiguringindependentsets-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://ebooks.iospress.nl/doi/10.3233/FAIA230303\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'christen-eriksson-katz-muise-petrov-pommerening-seipp-sievers-etal-parisplanningalgorithmsforreconfiguringindependentsets-2023', 'https://ebooks.iospress.nl/doi/10.3233/FAIA230303', event);\">\n    PARIS: Planning Algorithms for Reconfiguring Independent Sets.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Christen, R.; Eriksson, S.; Katz, M.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Petrov, A.; Pommerening, F.; Seipp, J.; Sievers, S.;  and Speck, D.\n</span>\n\n  \n\n</span>\n\n  In <i>26th European Conference on Artificial Intelligence</i>.  2023.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://ebooks.iospress.nl/doi/10.3233/FAIA230303\"\n     onclick=\"log_download('christen-eriksson-katz-muise-petrov-pommerening-seipp-sievers-etal-parisplanningalgorithmsforreconfiguringindependentsets-2023', 'https://ebooks.iospress.nl/doi/10.3233/FAIA230303', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"PARIS: Planning Algorithms for Reconfiguring Independent Sets [link]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/christen-eriksson-katz-muise-petrov-pommerening-seipp-sievers-etal-parisplanningalgorithmsforreconfiguringindependentsets-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>4 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('christen-eriksson-katz-muise-petrov-pommerening-seipp-sievers-etal-parisplanningalgorithmsforreconfiguringindependentsets-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{christen-ecai-2023,\n  title     = {PARIS: Planning Algorithms for Reconfiguring Independent Sets},\n  author    = {Remo Christen and Salome Eriksson and Michael Katz and Christian Muise and Alice Petrov and Florian Pommerening and Jendrik Seipp and Silvan Sievers and David Speck},\n  booktitle = {26th European Conference on Artificial Intelligence},\n  year      = {2023},\n  abstract  = {Combinatorial reconfiguration studies how one solution of a combinatorial problem can be transformed into another. The transformation can only make small local changes and may not leave the solution space. An important example is the independent set reconfiguration (ISR) problem, where an independent set of a graph (a subset of its vertices without edges between them) has to be transformed into another one by a sequence of modifications that remove a vertex or add another that is not adjacent to any vertex in the set. The 1st Combinatorial Reconfiguration Challenge (CoRe Challenge 2022) was a competition focused on the ISR problem. The PARIS team participated with two solvers that model the ISR problem as a planning problem and employ different planning techniques for solving it. The solvers successfully competed in the challenge and were awarded 4 first, 3 second, and 3 third places across 9 tracks. In this work, we show how to model ISR problems as planning tasks and describe the planning techniques used by these solvers. For a fair comparison to competing ISR approaches, we re-run the entire competition under equal computational conditions. Besides showcasing the success of planning technology, we hope that this work will create a cross-fertilization of the two research fields.},\n  url_paper = {https://ebooks.iospress.nl/doi/10.3233/FAIA230303}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Combinatorial reconfiguration studies how one solution of a combinatorial problem can be transformed into another. The transformation can only make small local changes and may not leave the solution space. An important example is the independent set reconfiguration (ISR) problem, where an independent set of a graph (a subset of its vertices without edges between them) has to be transformed into another one by a sequence of modifications that remove a vertex or add another that is not adjacent to any vertex in the set. The 1st Combinatorial Reconfiguration Challenge (CoRe Challenge 2022) was a competition focused on the ISR problem. The PARIS team participated with two solvers that model the ISR problem as a planning problem and employ different planning techniques for solving it. The solvers successfully competed in the challenge and were awarded 4 first, 3 second, and 3 third places across 9 tracks. In this work, we show how to model ISR problems as planning tasks and describe the planning techniques used by these solvers. For a fair comparison to competing ISR approaches, we re-run the entire competition under equal computational conditions. Besides showcasing the success of planning technology, we hope that this work will create a cross-fertilization of the two research fields.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"petrov-muise-planningforproofs-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Planning for Proofs.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Petrov, A.;  and <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>Scheduling and Planning Applications woRKshop</i>.  2023.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/petrov-muise-planningforproofs-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('petrov-muise-planningforproofs-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{petrov-spark-2023,\n  title     = {Planning for Proofs},\n  author    = {Alice Petrov and Christian Muise},\n  booktitle = {Scheduling and Planning Applications woRKshop},\n  year      = {2023},\n  abstract  = {This paper explores the application of automated planning to automated theorem proving, which is a branch of automated reasoning concerned with the development of algorithms and computer programs to construct mathematical proofs. In particular, we investigate the use of planning to construct elementary proofs in abstract algebra, which provides a rigorous and axiomatic framework for studying algebraic structures such as groups, rings, fields, and modules. We implement basic implications, equalities, and rules in both deterministic and non-deterministic domains to model commutative rings and deduce elementary results about them. The success of this initial implementation suggests that the well-established techniques seen in automated planning are applicable to the relatively newer field of automated theorem proving. Likewise, automated theorem proving provides a new, challenging domain for automated planning.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This paper explores the application of automated planning to automated theorem proving, which is a branch of automated reasoning concerned with the development of algorithms and computer programs to construct mathematical proofs. In particular, we investigate the use of planning to construct elementary proofs in abstract algebra, which provides a rigorous and axiomatic framework for studying algebraic structures such as groups, rings, fields, and modules. We implement basic implications, equalities, and rules in both deterministic and non-deterministic domains to model commutative rings and deduce elementary results about them. The success of this initial implementation suggests that the well-established techniques seen in automated planning are applicable to the relatively newer field of automated theorem proving. Likewise, automated theorem proving provides a new, challenging domain for automated planning.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"klassen-muise-mcilraith-towardshumanawareaiviaplanningwithepistemicpreferences-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://openreview.net/pdf?id=nkn1rcI6wd\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'klassen-muise-mcilraith-towardshumanawareaiviaplanningwithepistemicpreferences-2023', 'https://openreview.net/pdf?id=nkn1rcI6wd', event);\">\n    Towards Human-Aware AI via Planning with Epistemic Preferences.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Klassen, T. Q.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>;  and McIlraith, S. A.\n</span>\n\n  \n\n</span>\n\n  In <i>International Workshop on Human-Aware and Explainable Planning</i>.  2023.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://openreview.net/pdf?id=nkn1rcI6wd\"\n     onclick=\"log_download('klassen-muise-mcilraith-towardshumanawareaiviaplanningwithepistemicpreferences-2023', 'https://openreview.net/pdf?id=nkn1rcI6wd', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Towards Human-Aware AI via Planning with Epistemic Preferences [link]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/klassen-muise-mcilraith-towardshumanawareaiviaplanningwithepistemicpreferences-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('klassen-muise-mcilraith-towardshumanawareaiviaplanningwithepistemicpreferences-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{klassen-haxp-2023,\n  title     = {Towards Human-Aware AI via Planning with Epistemic Preferences},\n  author    = {Toryn Q. Klassen and Christian Muise and Sheila A. McIlraith},\n  booktitle = {International Workshop on Human-Aware and Explainable Planning},\n  year      = {2023},\n  url_paper = {https://openreview.net/pdf?id=nkn1rcI6wd},\n  abstract  = {Within the field of automated planning, two areas of study are planning with preferences, and epistemic planning. Planning with preferences involves generating plans that optimize for properties of the plan instead of, or in addition to, trying to reach a fixed goal. Epistemic planning allows for planning over the knowledge or belief states of one or more agents for the purpose of achieving epistemic goals (where agents have particular states of knowledge or belief). In this paper we motivate and explore the task of planning with epistemic preferences, proposing a method by which existing automated planning techniques can be combined for this purpose. Epistemic preferences may better allow for representing what humans want, and have benefits for AI safety.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Within the field of automated planning, two areas of study are planning with preferences, and epistemic planning. Planning with preferences involves generating plans that optimize for properties of the plan instead of, or in addition to, trying to reach a fixed goal. Epistemic planning allows for planning over the knowledge or belief states of one or more agents for the purpose of achieving epistemic goals (where agents have particular states of knowledge or belief). In this paper we motivate and explore the task of planning with epistemic preferences, proposing a method by which existing automated planning techniques can be combined for this purpose. Epistemic preferences may better allow for representing what humans want, and have benefits for AI safety.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"klassen-muise-mcilraith-planningwithepistemicpreferences-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://proceedings.kr.org/2023/76/kr2023-0076-klassen-et-al.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'klassen-muise-mcilraith-planningwithepistemicpreferences-2023', 'https://proceedings.kr.org/2023/76/kr2023-0076-klassen-et-al.pdf', event);\">\n    Planning with Epistemic Preferences.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Klassen, T. Q.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>;  and McIlraith, S. A.\n</span>\n\n  \n\n</span>\n\n  In <i>International Conference on Principles of Knowledge Representation and Reasoning</i>, pages 752–756.  2023.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://proceedings.kr.org/2023/76/kr2023-0076-klassen-et-al.pdf\"\n     onclick=\"log_download('klassen-muise-mcilraith-planningwithepistemicpreferences-2023', 'https://proceedings.kr.org/2023/76/kr2023-0076-klassen-et-al.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Planning with Epistemic Preferences [pdf]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/klassen-muise-mcilraith-planningwithepistemicpreferences-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>6 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('klassen-muise-mcilraith-planningwithepistemicpreferences-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{klassen-kr-2023,\n  title     = {Planning with Epistemic Preferences},\n  author    = {Toryn Q. Klassen and Christian Muise and Sheila A. McIlraith},\n  booktitle = {International Conference on Principles of Knowledge Representation and Reasoning},\n  pages     = {752--756},\n  year      = {2023},\n  url_paper = {https://proceedings.kr.org/2023/76/kr2023-0076-klassen-et-al.pdf},\n  abstract  = {Within the field of automated planning, two areas of study are planning with preferences and epistemic planning. Planning with preferences involves generating plans that optimize for properties of the plan instead of, or in addition to, trying to reach a fixed goal. Epistemic planning allows for planning over the knowledge or belief states of one or more agents for the purpose of achieving epistemic goals (where agents have particular states of knowledge or belief). In this paper we motivate and explore the task of planning with epistemic preferences, proposing a method by which existing automated planning techniques can be combined for this purpose.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Within the field of automated planning, two areas of study are planning with preferences and epistemic planning. Planning with preferences involves generating plans that optimize for properties of the plan instead of, or in addition to, trying to reach a fixed goal. Epistemic planning allows for planning over the knowledge or belief states of one or more agents for the purpose of achieving epistemic goals (where agents have particular states of knowledge or belief). In this paper we motivate and explore the task of planning with epistemic preferences, proposing a method by which existing automated planning techniques can be combined for this purpose.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"chakraborti-kang-muise-sreedharan-walker-szafir-williams-tobyatoolforexploringdatainacademicsurveypapers-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://arxiv.org/abs/2306.10051\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'chakraborti-kang-muise-sreedharan-walker-szafir-williams-tobyatoolforexploringdatainacademicsurveypapers-2023', 'https://arxiv.org/abs/2306.10051', event);\">\n    TOBY: A Tool for Exploring Data in Academic Survey Papers.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Chakraborti, T.; Kang, J.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Sreedharan, S.; Walker, M.; Szafir, D.;  and Williams, T.\n</span>\n\n  \n\n</span>\n\n  In <i>arXiv</i>.  2023.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://arxiv.org/abs/2306.10051\"\n     onclick=\"log_download('chakraborti-kang-muise-sreedharan-walker-szafir-williams-tobyatoolforexploringdatainacademicsurveypapers-2023', 'https://arxiv.org/abs/2306.10051', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"TOBY: A Tool for Exploring Data in Academic Survey Papers [link]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/chakraborti-kang-muise-sreedharan-walker-szafir-williams-tobyatoolforexploringdatainacademicsurveypapers-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('chakraborti-kang-muise-sreedharan-walker-szafir-williams-tobyatoolforexploringdatainacademicsurveypapers-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{chakraborti-arxiv-2023,\n  title     = {TOBY: A Tool for Exploring Data in Academic Survey Papers},\n  author    = {Tathagata Chakraborti and Jungkoo Kang and Christian Muise and Sarath Sreedharan and Michael Walker and Daniel Szafir and Tom Williams},\n  booktitle = {arXiv},\n  year      = {2023},\n  url_paper = {https://arxiv.org/abs/2306.10051},\n  abstract  = {This paper describes TOBY, a visualization tool that helps a user explore the contents of an academic survey paper. The visualization consists of four components: a hierarchical view of taxonomic data in the survey, a document similarity view in the space of taxonomic classes, a network view of citations, and a new paper recommendation tool. In this paper, we will discuss these features in the context of three separate deployments of the tool.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This paper describes TOBY, a visualization tool that helps a user explore the contents of an academic survey paper. The visualization consists of four components: a hierarchical view of taxonomic data in the survey, a document similarity view in the space of taxonomic classes, a network view of citations, and a new paper recommendation tool. In this paper, we will discuss these features in the context of three separate deployments of the tool.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2022\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2022')\"\n      onkeypress=\"toggleGroup('group_2022')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2022</span>\n      <span class=\"bibbase_group_count\">\n        \n        (14)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"asai-kajino-fukunaga-muise-classicalplanningindeeplatentspace-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.jair.org/index.php/jair/article/view/13768\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'asai-kajino-fukunaga-muise-classicalplanningindeeplatentspace-2022', 'https://www.jair.org/index.php/jair/article/view/13768', event);\">\n    Classical Planning in Deep Latent Space.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Asai, M.; Kajino, H.; Fukunaga, A.;  and <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Artificial Intelligence Research</i>, 74: 1599–1686.  2022.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.jair.org/index.php/jair/article/view/13768\"\n     onclick=\"log_download('asai-kajino-fukunaga-muise-classicalplanningindeeplatentspace-2022', 'https://www.jair.org/index.php/jair/article/view/13768', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Classical Planning in Deep Latent Space [link]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1613/jair.1.13768\"\n     onclick=\"log_download('asai-kajino-fukunaga-muise-classicalplanningindeeplatentspace-2022', 'http://doi.org/10.1613/jair.1.13768', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/asai-kajino-fukunaga-muise-classicalplanningindeeplatentspace-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>7 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('asai-kajino-fukunaga-muise-classicalplanningindeeplatentspace-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{DBLP:journals/jair/AsaiKFM22,\n  author    = {Masataro Asai and\n               Hiroshi Kajino and\n               Alex Fukunaga and\n               Christian Muise},\n  title     = {Classical Planning in Deep Latent Space},\n  journal   = {Journal of Artificial Intelligence Research},\n  volume    = {74},\n  pages     = {1599--1686},\n  year      = {2022},\n  doi       = {10.1613/jair.1.13768},\n  url_paper = {https://www.jair.org/index.php/jair/article/view/13768},\n  abstract  = {Current domain-independent, classical planners require symbolic models of the problem domain and instance as input, resulting in a knowledge acquisition bottleneck. Meanwhile, although deep learning has achieved significant success in many fields, the knowledge is encoded in a subsymbolic representation which is incompatible with symbolic systems such as planners. We propose Latplan, an unsupervised architecture combining deep learning and classical planning. Given only an unlabeled set of image pairs showing a subset of transitions allowed in the environment (training inputs), Latplan learns a complete propositional PDDL action model of the environment. Later, when a pair of images representing the initial and the goal states (planning inputs) is given, Latplan finds a plan to the goal state in a symbolic latent space and returns a visualized plan execution. We evaluate Latplan using image-based versions of 6 planning domains: 8-puzzle, 15-Puzzle, Blocksworld, Sokoban and Two variations of LightsOut.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Current domain-independent, classical planners require symbolic models of the problem domain and instance as input, resulting in a knowledge acquisition bottleneck. Meanwhile, although deep learning has achieved significant success in many fields, the knowledge is encoded in a subsymbolic representation which is incompatible with symbolic systems such as planners. We propose Latplan, an unsupervised architecture combining deep learning and classical planning. Given only an unlabeled set of image pairs showing a subset of transitions allowed in the environment (training inputs), Latplan learns a complete propositional PDDL action model of the environment. Later, when a pair of images representing the initial and the goal states (planning inputs) is given, Latplan finds a plan to the goal state in a symbolic latent space and returns a visualized plan execution. We evaluate Latplan using image-based versions of 6 planning domains: 8-puzzle, 15-Puzzle, Blocksworld, Sokoban and Two variations of LightsOut.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"liu-palacios-muise-aplanningbasedneuralsymbolicapproachforembodiedinstructionfollowing-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://embodied-ai.org/papers/2022/15.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'liu-palacios-muise-aplanningbasedneuralsymbolicapproachforembodiedinstructionfollowing-2022', 'https://embodied-ai.org/papers/2022/15.pdf', event);\">\n    A Planning based Neural-Symbolic Approach for Embodied Instruction Following.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Liu, X.; Palacios, H.;  and <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>CVPR Embodied AI Workshop</i>.  2022.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://embodied-ai.org/papers/2022/15.pdf\"\n     onclick=\"log_download('liu-palacios-muise-aplanningbasedneuralsymbolicapproachforembodiedinstructionfollowing-2022', 'https://embodied-ai.org/papers/2022/15.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"A Planning based Neural-Symbolic Approach for Embodied Instruction Following [pdf]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/liu-palacios-muise-aplanningbasedneuralsymbolicapproachforembodiedinstructionfollowing-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>7 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('liu-palacios-muise-aplanningbasedneuralsymbolicapproachforembodiedinstructionfollowing-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{liu-eai-2022,\n  title     = {A Planning based Neural-Symbolic Approach for Embodied Instruction Following},\n  author    = {Xiaotian Liu and Hector Palacios and Christian Muise},\n  booktitle = {CVPR Embodied AI Workshop},\n  year      = {2022},\n  url_paper = {https://embodied-ai.org/papers/2022/15.pdf},\n  abstract  = {The ALFRED environment features an embodied agent following instructions and accomplishing tasks in simulated home environments. However, end-to-end deep learning methods struggle at these tasks due to long-horizon and sparse rewards. In this work, we propose a principled neural-symbolic approach combining symbolic planning and deep-learning methods for visual perception and NL processing. The symbolic model is enriched as exploration progress until a full plan can be obtained. New perceptions are added to a discrete graph representation that is used for producing new planning problems. Empirical results demonstrate that our approach can achieve high scalability with SOTA performance of 36.04% unseen success rate in the ALFRED benchmark. Our work builds a foundation for a neural-symbolic approach that can act in unstructured environments when the set of skills and possible relationships is known.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The ALFRED environment features an embodied agent following instructions and accomplishing tasks in simulated home environments. However, end-to-end deep learning methods struggle at these tasks due to long-horizon and sparse rewards. In this work, we propose a principled neural-symbolic approach combining symbolic planning and deep-learning methods for visual perception and NL processing. The symbolic model is enriched as exploration progress until a full plan can be obtained. New perceptions are added to a discrete graph representation that is used for producing new planning problems. Empirical results demonstrate that our approach can achieve high scalability with SOTA performance of 36.04% unseen success rate in the ALFRED benchmark. Our work builds a foundation for a neural-symbolic approach that can act in unstructured environments when the set of skills and possible relationships is known.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"callanan-venezia-armstrong-paredes-kang-chakraborti-muise-macqaunifiedlibraryforactionmodelacquisition-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://icaps22.icaps-conference.org/demos/ICAPS_2022_paper_378.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'callanan-venezia-armstrong-paredes-kang-chakraborti-muise-macqaunifiedlibraryforactionmodelacquisition-2022', 'http://icaps22.icaps-conference.org/demos/ICAPS_2022_paper_378.pdf', event);\">\n    MACQ: A Unified Library for Action Model Acquisition.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Callanan, E.; Venezia, R. D.; Armstrong, V.; Paredes, A.; Kang, J.; Chakraborti, T.;  and <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>International Conference on Automated Planning and Scheduling: System Demonstrations</i>.  2022.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://icaps22.icaps-conference.org/demos/ICAPS_2022_paper_378.pdf\"\n     onclick=\"log_download('callanan-venezia-armstrong-paredes-kang-chakraborti-muise-macqaunifiedlibraryforactionmodelacquisition-2022', 'http://icaps22.icaps-conference.org/demos/ICAPS_2022_paper_378.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"MACQ: A Unified Library for Action Model Acquisition [pdf]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/callanan-venezia-armstrong-paredes-kang-chakraborti-muise-macqaunifiedlibraryforactionmodelacquisition-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>7 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('callanan-venezia-armstrong-paredes-kang-chakraborti-muise-macqaunifiedlibraryforactionmodelacquisition-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{callanan-icapsdemo-2022,\n  title     = {MACQ: A Unified Library for Action Model Acquisition},\n  author    = {Ethan Callanan and Rebecca De Venezia and Victoria Armstrong and Alison Paredes and Jungkoo Kang and Tathagata Chakraborti and Christian Muise},\n  booktitle = {International Conference on Automated Planning and Scheduling: System Demonstrations},\n  year      = {2022},\n  url_paper = {http://icaps22.icaps-conference.org/demos/ICAPS_2022_paper_378.pdf},\n  abstract  = {For over three decades, the planning community has explored countless methods for data-driven model acquisition. These range in sophistication (e.g., simple set operations to full-blown reformulations), methodology (e.g., logic-based -vs- planning-based), and assumptions (e.g., fully -vs- partially observable). With no fewer than 43 publications in the space, it can be overwhelming to understand what approach could or should be applied in a new setting. We present a holistic characterization of the action model acquisition space and further introduce a unifying framework for automated action model acquisition. We have re-implemented some of the landmark approaches in the area, and our characterization of all the techniques offers deep insight into the research opportunities that remain; i.e., those settings where no technique is capable of solving.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  For over three decades, the planning community has explored countless methods for data-driven model acquisition. These range in sophistication (e.g., simple set operations to full-blown reformulations), methodology (e.g., logic-based -vs- planning-based), and assumptions (e.g., fully -vs- partially observable). With no fewer than 43 publications in the space, it can be overwhelming to understand what approach could or should be applied in a new setting. We present a holistic characterization of the action model acquisition space and further introduce a unifying framework for automated action model acquisition. We have re-implemented some of the landmark approaches in the area, and our characterization of all the techniques offers deep insight into the research opportunities that remain; i.e., those settings where no technique is capable of solving.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"coulter-ilie-tibando-muise-planningtechforplanningpedagogy-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://icaps22.icaps-conference.org/demos/ICAPS_2022_paper_376.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'coulter-ilie-tibando-muise-planningtechforplanningpedagogy-2022', 'http://icaps22.icaps-conference.org/demos/ICAPS_2022_paper_376.pdf', event);\">\n    Planning Tech for Planning Pedagogy.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Coulter, A.; Ilie, T.; Tibando, R.;  and <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>International Conference on Automated Planning and Scheduling: System Demonstrations</i>.  2022.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://icaps22.icaps-conference.org/demos/ICAPS_2022_paper_376.pdf\"\n     onclick=\"log_download('coulter-ilie-tibando-muise-planningtechforplanningpedagogy-2022', 'http://icaps22.icaps-conference.org/demos/ICAPS_2022_paper_376.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Planning Tech for Planning Pedagogy [pdf]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/coulter-ilie-tibando-muise-planningtechforplanningpedagogy-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('coulter-ilie-tibando-muise-planningtechforplanningpedagogy-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{coulter-icapsdemo-2022,\n  title     = {Planning Tech for Planning Pedagogy},\n  author    = {Alex Coulter and Teo Ilie and Renee Tibando and Christian Muise},\n  booktitle = {International Conference on Automated Planning and Scheduling: System Demonstrations},\n  year      = {2022},\n  url_paper = {http://icaps22.icaps-conference.org/demos/ICAPS_2022_paper_376.pdf},\n  abstract  = {We demonstrate the power planning techniques can have for the task of analyzing planning solutions in a classroom setting. Using the common assignment strategy of asking students to develop PDDL given an English description of a domain, we consider how a variety of planning methods (existing and new) can provide analytic support for teaching staff to understand which errors were made in student models. The work has already had a direct and practical impact, being deployed in a classroom setting to assess the correctness of student-authored planning models.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  We demonstrate the power planning techniques can have for the task of analyzing planning solutions in a classroom setting. Using the common assignment strategy of asking students to develop PDDL given an English description of a domain, we consider how a variety of planning methods (existing and new) can provide analytic support for teaching staff to understand which errors were made in student models. The work has already had a direct and practical impact, being deployed in a classroom setting to assess the correctness of student-authored planning models.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"muise-pommerening-seipp-katz-planutilsbringingplanningtothemasses-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://icaps22.icaps-conference.org/demos/ICAPS_2022_paper_377.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'muise-pommerening-seipp-katz-planutilsbringingplanningtothemasses-2022', 'http://icaps22.icaps-conference.org/demos/ICAPS_2022_paper_377.pdf', event);\">\n    PLANUTILS: Bringing Planning to the Masses.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Pommerening, F.; Seipp, J.;  and Katz, M.\n</span>\n\n  \n\n</span>\n\n  In <i>International Conference on Automated Planning and Scheduling: System Demonstrations</i>.  2022.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://icaps22.icaps-conference.org/demos/ICAPS_2022_paper_377.pdf\"\n     onclick=\"log_download('muise-pommerening-seipp-katz-planutilsbringingplanningtothemasses-2022', 'http://icaps22.icaps-conference.org/demos/ICAPS_2022_paper_377.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"PLANUTILS: Bringing Planning to the Masses [pdf]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-pommerening-seipp-katz-planutilsbringingplanningtothemasses-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>5 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-pommerening-seipp-katz-planutilsbringingplanningtothemasses-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{muise-icapsdemo-2022,\n  title     = {PLANUTILS: Bringing Planning to the Masses},\n  author    = {Christian Muise and Florian Pommerening and Jendrik Seipp and Michael Katz},\n  booktitle = {International Conference on Automated Planning and Scheduling: System Demonstrations},\n  year      = {2022},\n  url_paper = {http://icaps22.icaps-conference.org/demos/ICAPS_2022_paper_377.pdf},\n  abstract  = {PLANUTILS is a general library for setting up Linux-based environments for developing, running, and evaluating planners. Over the last decades, the planning community has produced countless solvers for various planning formalisms, as well as many other tools to help the planning practitioner. From state-of-the-art planners, over validators, to parsing libraries, the planning ecosystem has grown quite large. In the demo, we highlight an effort that aims to unify this ecosystem and make it seamless for users to get started with what the ICAPS community has to offer.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  PLANUTILS is a general library for setting up Linux-based environments for developing, running, and evaluating planners. Over the last decades, the planning community has produced countless solvers for various planning formalisms, as well as many other tools to help the planning practitioner. From state-of-the-art planners, over validators, to parsing libraries, the planning ecosystem has grown quite large. In the demo, we highlight an effort that aims to unify this ecosystem and make it seamless for users to get started with what the ICAPS community has to offer.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"simon-muise-tattletalestorytellingwithplanningandlargelanguagemodels-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://icaps22.icaps-conference.org/workshops/SPARK/papers/spark2022_paper_2.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'simon-muise-tattletalestorytellingwithplanningandlargelanguagemodels-2022', 'http://icaps22.icaps-conference.org/workshops/SPARK/papers/spark2022_paper_2.pdf', event);\">\n    TattleTale: Storytelling with Planning and Large Language Models.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Simon, N.;  and <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>ICAPS Workshop on Scheduling and Planning Applications woRKshop</i>.  2022.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://icaps22.icaps-conference.org/workshops/SPARK/papers/spark2022_paper_2.pdf\"\n     onclick=\"log_download('simon-muise-tattletalestorytellingwithplanningandlargelanguagemodels-2022', 'http://icaps22.icaps-conference.org/workshops/SPARK/papers/spark2022_paper_2.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"TattleTale: Storytelling with Planning and Large Language Models [pdf]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/simon-muise-tattletalestorytellingwithplanningandlargelanguagemodels-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>11 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('simon-muise-tattletalestorytellingwithplanningandlargelanguagemodels-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{simon-spark-2022,\n  title     = {TattleTale: Storytelling with Planning and Large Language Models},\n  author    = {Nisha Simon and Christian Muise},\n  booktitle = {ICAPS Workshop on Scheduling and Planning Applications woRKshop},\n  year      = {2022},\n  url_paper = {http://icaps22.icaps-conference.org/workshops/SPARK/papers/spark2022_paper_2.pdf},\n  abstract  = {We explore how automated planning can be applied to Natural Language text generation in order to create narratives (stories) that are coherent and believable. While Large Language Models (LLMs) such as GPT-3 can be used for narrative generation based on given input prompts, they lack coherence and can be prone to repetition and stilted language. We demonstrate the use of a planning model that provides scaffolding to an LLM so that its language generation is contextdependent in order to create more coherent and believable stories in a variety of domains. After manually extracting characters, objects, and locations from the story source, we create domain and problem encoding that captures the mechanics of the story. The output of a planner, taken one action at a time, is fed to the LLM to generate a narrative. We f ind that almost all nouns (characters, objects, and locations) and verbs (actions) of the plan are reflected in the generated story, and the resulting narrative is more coherent than stories that are generated using only plain text prompts to the LLM. Finally, gathering, curating, and modelling the source stories in PDDL is an additional contribution of our work that will be released publicly. Our work represents a key first step towards the novel application of planning technology to a neuro-symbolic approach for effective story generation}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  We explore how automated planning can be applied to Natural Language text generation in order to create narratives (stories) that are coherent and believable. While Large Language Models (LLMs) such as GPT-3 can be used for narrative generation based on given input prompts, they lack coherence and can be prone to repetition and stilted language. We demonstrate the use of a planning model that provides scaffolding to an LLM so that its language generation is contextdependent in order to create more coherent and believable stories in a variety of domains. After manually extracting characters, objects, and locations from the story source, we create domain and problem encoding that captures the mechanics of the story. The output of a planner, taken one action at a time, is fed to the LLM to generate a narrative. We f ind that almost all nouns (characters, objects, and locations) and verbs (actions) of the plan are reflected in the generated story, and the resulting narrative is more coherent than stories that are generated using only plain text prompts to the LLM. Finally, gathering, curating, and modelling the source stories in PDDL is an additional contribution of our work that will be released publicly. Our work represents a key first step towards the novel application of planning technology to a neuro-symbolic approach for effective story generation\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"coulter-ilie-tibando-muise-theoryalignmentviaaclassicalencodingofregularbisimulation-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://icaps22.icaps-conference.org/workshops/KEPS/KEPS-22_paper_7781.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'coulter-ilie-tibando-muise-theoryalignmentviaaclassicalencodingofregularbisimulation-2022', 'http://icaps22.icaps-conference.org/workshops/KEPS/KEPS-22_paper_7781.pdf', event);\">\n    Theory Alignment via a Classical Encoding of Regular Bisimulation.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Coulter, A.; Ilie, T.; Tibando, R.;  and <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>Workshop on Knowledge Engineering for Planning and Scheduling</i>.  2022.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://icaps22.icaps-conference.org/workshops/KEPS/KEPS-22_paper_7781.pdf\"\n     onclick=\"log_download('coulter-ilie-tibando-muise-theoryalignmentviaaclassicalencodingofregularbisimulation-2022', 'http://icaps22.icaps-conference.org/workshops/KEPS/KEPS-22_paper_7781.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Theory Alignment via a Classical Encoding of Regular Bisimulation [pdf]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/coulter-ilie-tibando-muise-theoryalignmentviaaclassicalencodingofregularbisimulation-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>5 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('coulter-ilie-tibando-muise-theoryalignmentviaaclassicalencodingofregularbisimulation-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{coulter-keps-2022,\n  title     = {Theory Alignment via a Classical Encoding of Regular Bisimulation},\n  author    = {Alex Coulter and Teo Ilie and Renee Tibando and Christian Muise},\n  booktitle = {Workshop on Knowledge Engineering for Planning and Scheduling},\n  year      = {2022},\n  url_paper = {http://icaps22.icaps-conference.org/workshops/KEPS/KEPS-22_paper_7781.pdf},\n  abstract  = {Bisimulation, at its core, is a means of studying the alignment between two dynamical systems. It has been used to great effect in the planning community for heuristic computation; simulating the full state space in the space of abstractions (merge-and-shrink heuristics). Here, we consider the direct task of theory alignment– assessing if two planning problems are “equivalent”– through the lens of regular bisimulation. We accomplish the task through a novel encoding that merges the two theories as a new planning problem, where the encoded problem is unsolvable if and only if the two theories are a regular bisimulation. We demonstrate that modern planners are capable of solving many of these encodings, and the solutions (if plans exist) provide a rich explanation as to whytwomodels differ. The work has already had a direct and practical impact, being deployed in a classroom setting to assess the correctness of student-authored planning models as compared against a reference solution. Our solution has a direct impact on being able to verify if a candidate planning model matches a known specification, and opens the door to model verification through planning techniques.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Bisimulation, at its core, is a means of studying the alignment between two dynamical systems. It has been used to great effect in the planning community for heuristic computation; simulating the full state space in the space of abstractions (merge-and-shrink heuristics). Here, we consider the direct task of theory alignment– assessing if two planning problems are “equivalent”– through the lens of regular bisimulation. We accomplish the task through a novel encoding that merges the two theories as a new planning problem, where the encoded problem is unsolvable if and only if the two theories are a regular bisimulation. We demonstrate that modern planners are capable of solving many of these encodings, and the solutions (if plans exist) provide a rich explanation as to whytwomodels differ. The work has already had a direct and practical impact, being deployed in a classroom setting to assess the correctness of student-authored planning models as compared against a reference solution. Our solution has a direct impact on being able to verify if a candidate planning model matches a known specification, and opens the door to model verification through planning techniques.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"cruse-muise-discretetimeseriesclusteringanddelineationatreebasedapproachtolineartemporallogicdiscovery-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://openreview.net/forum?id=nil-578ERCL\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'cruse-muise-discretetimeseriesclusteringanddelineationatreebasedapproachtolineartemporallogicdiscovery-2022', 'https://openreview.net/forum?id=nil-578ERCL', event);\">\n    Discrete Time Series Clustering and Delineation: A Tree-Based Approach to Linear Temporal Logic Discovery.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Cruse, B.;  and <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>The International Workshop of Explainable AI Planning</i>.  2022.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://openreview.net/forum?id=nil-578ERCL\"\n     onclick=\"log_download('cruse-muise-discretetimeseriesclusteringanddelineationatreebasedapproachtolineartemporallogicdiscovery-2022', 'https://openreview.net/forum?id=nil-578ERCL', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Discrete Time Series Clustering and Delineation: A Tree-Based Approach to Linear Temporal Logic Discovery [link]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/cruse-muise-discretetimeseriesclusteringanddelineationatreebasedapproachtolineartemporallogicdiscovery-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('cruse-muise-discretetimeseriesclusteringanddelineationatreebasedapproachtolineartemporallogicdiscovery-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{cruse-xaip-2022,\n  title     = {Discrete Time Series Clustering and Delineation: A Tree-Based Approach to Linear Temporal Logic Discovery},\n  author    = {Brennan Cruse and Christian Muise},\n  booktitle = {The International Workshop of Explainable AI Planning},\n  year      = {2022},\n  url_paper = {https://openreview.net/forum?id=nil-578ERCL},\n  abstract  = {Inferring temporal logic specifications from plan traces can offer significant insight into several aspects of planning such as goal recognition, policy summarization, and system dynamic modelling. Prior work in this area has predominantly focused on the identification of specifications that satisfy all plan traces within a set, however more recently, contrastive approaches concerning the delineation of two sets have also been established. While these approaches are effective in their defined scope, they assume the existence of only one or two behavioural clusters. In this paper, we re-imagine contrastive specification learning by proposing a novel tree generation technique which allows k clusters to be discovered. By embracing a Monte Carlo node-splitting approach, our algorithm seeks balance to contrastively divide any given set of plan traces into two sets with an accompanying temporal logic specification satisfying one of the sets. Recursing this procedure, we demonstrate the effectiveness of our approach to cluster and delineate plan traces, allowing temporal logic specifications to evoke insight at each level of the resulting tree.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Inferring temporal logic specifications from plan traces can offer significant insight into several aspects of planning such as goal recognition, policy summarization, and system dynamic modelling. Prior work in this area has predominantly focused on the identification of specifications that satisfy all plan traces within a set, however more recently, contrastive approaches concerning the delineation of two sets have also been established. While these approaches are effective in their defined scope, they assume the existence of only one or two behavioural clusters. In this paper, we re-imagine contrastive specification learning by proposing a novel tree generation technique which allows k clusters to be discovered. By embracing a Monte Carlo node-splitting approach, our algorithm seeks balance to contrastively divide any given set of plan traces into two sets with an accompanying temporal logic specification satisfying one of the sets. Recursing this procedure, we demonstrate the effectiveness of our approach to cluster and delineate plan traces, allowing temporal logic specifications to evoke insight at each level of the resulting tree.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"sreedharan-muise-kambhampati-whydidyoudothatgeneralizingcausallinkexplanationstofullyobservablenondeterministicplanningproblems-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://openreview.net/forum?id=D44ytXrLXuS\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'sreedharan-muise-kambhampati-whydidyoudothatgeneralizingcausallinkexplanationstofullyobservablenondeterministicplanningproblems-2022', 'https://openreview.net/forum?id=D44ytXrLXuS', event);\">\n    Why Did You Do That? Generalizing Causal Link Explanations to Fully Observable Non-Deterministic Planning Problems.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Sreedharan, S.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>;  and Kambhampati, S.\n</span>\n\n  \n\n</span>\n\n  In <i>The International Workshop of Explainable AI Planning</i>.  2022.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://openreview.net/forum?id=D44ytXrLXuS\"\n     onclick=\"log_download('sreedharan-muise-kambhampati-whydidyoudothatgeneralizingcausallinkexplanationstofullyobservablenondeterministicplanningproblems-2022', 'https://openreview.net/forum?id=D44ytXrLXuS', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Why Did You Do That? Generalizing Causal Link Explanations to Fully Observable Non-Deterministic Planning Problems [link]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/sreedharan-muise-kambhampati-whydidyoudothatgeneralizingcausallinkexplanationstofullyobservablenondeterministicplanningproblems-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('sreedharan-muise-kambhampati-whydidyoudothatgeneralizingcausallinkexplanationstofullyobservablenondeterministicplanningproblems-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{sreedharan-xaip-2022,\n  title     = {Why Did You Do That? Generalizing Causal Link Explanations to Fully Observable Non-Deterministic Planning Problems},\n  author    = {Sarath Sreedharan and Christian Muise and Subbarao Kambhampati},\n  booktitle = {The International Workshop of Explainable AI Planning},\n  year      = {2022},\n  url_paper = {https://openreview.net/forum?id=D44ytXrLXuS},\n  abstract  = {The problem of designing automated agents, particularly automated planning agents that can explain their decisions has been receiving a lot of attention in recent years. The field of explainable planning or XAIP has already made a lot of progress in recent years and many of them centered around the problem of explaining decisions derived for classical planning problems. As the field progresses there is interest in tackling problems from more complex planning formalisms. However, one important aspect to keep in mind as we start focusing on such settings is that the explanatory challenges we study in the context of classical planning problems do not disappear when we move to more general settings but are just magnified. As such, when we move to these more general settings, a significant challenge before us is to see how one could generalize the well-established methods studied in the context of classical planning problems to these new settings. To provide a concrete example for this new research program we will start with causal link explanations, one of the earliest and most widely used explanations techniques used in the context of policies generated for fully observable non-deterministic planning problems. This would see us generalizing a concept that was originally developed for a specific solution concept, i.e, sequential plans, and see them applied to a very different solution concept (i.e. policies). We will develop a compilation-based method for generating generalized causal link explanations and show how as the domain is limited to deterministic cases, our method would generate causal link chains as identified by earlier works.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The problem of designing automated agents, particularly automated planning agents that can explain their decisions has been receiving a lot of attention in recent years. The field of explainable planning or XAIP has already made a lot of progress in recent years and many of them centered around the problem of explaining decisions derived for classical planning problems. As the field progresses there is interest in tackling problems from more complex planning formalisms. However, one important aspect to keep in mind as we start focusing on such settings is that the explanatory challenges we study in the context of classical planning problems do not disappear when we move to more general settings but are just magnified. As such, when we move to these more general settings, a significant challenge before us is to see how one could generalize the well-established methods studied in the context of classical planning problems to these new settings. To provide a concrete example for this new research program we will start with causal link explanations, one of the earliest and most widely used explanations techniques used in the context of policies generated for fully observable non-deterministic planning problems. This would see us generalizing a concept that was originally developed for a specific solution concept, i.e, sequential plans, and see them applied to a very different solution concept (i.e. policies). We will develop a compilation-based method for generating generalized causal link explanations and show how as the domain is limited to deterministic cases, our method would generate causal link chains as identified by earlier works.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"klassen-mcilraith-muise-xu-planningtoavoidsideeffects-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://ojs.aaai.org/index.php/AAAI/article/view/21219\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'klassen-mcilraith-muise-xu-planningtoavoidsideeffects-2022', 'https://ojs.aaai.org/index.php/AAAI/article/view/21219', event);\">\n    Planning to Avoid Side Effects.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Klassen, T. Q.; McIlraith, S. A.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>;  and Xu, J.\n</span>\n\n  \n\n</span>\n\n  In <i>AAAI Conference on Artificial Intelligence</i>, pages 9830–9839.  2022.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://ojs.aaai.org/index.php/AAAI/article/view/21219\"\n     onclick=\"log_download('klassen-mcilraith-muise-xu-planningtoavoidsideeffects-2022', 'https://ojs.aaai.org/index.php/AAAI/article/view/21219', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Planning to Avoid Side Effects [link]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/klassen-mcilraith-muise-xu-planningtoavoidsideeffects-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>11 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('klassen-mcilraith-muise-xu-planningtoavoidsideeffects-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{klassen-aaai-2022,\n  title     = {Planning to Avoid Side Effects},\n  author    = {Toryn Q. Klassen and Sheila A. McIlraith and Christian Muise and Jarvis Xu},\n  booktitle = {AAAI Conference on Artificial Intelligence},\n  pages     = {9830--9839},\n  year      = {2022},\n  url_paper = {https://ojs.aaai.org/index.php/AAAI/article/view/21219},\n  abstract  = {In sequential decision making, objective specifications are often underspecified or incomplete, neglecting to take into account potential (negative) side effects. Executing plans without consideration of their side effects can lead to catastrophic outcomes -- a concern recently raised in relation to the safety of AI. In this paper we investigate how to avoid side effects in a symbolic planning setting. We study the notion of minimizing side effects in the context of a planning environment where multiple independent agents co-exist. We define (classes of) negative side effects in terms of their effect on the agency of those other agents. Finally, we show how plans which minimize side effects of different types can be computed via compilations to cost-optimizing symbolic planning, and investigate experimentally.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  In sequential decision making, objective specifications are often underspecified or incomplete, neglecting to take into account potential (negative) side effects. Executing plans without consideration of their side effects can lead to catastrophic outcomes – a concern recently raised in relation to the safety of AI. In this paper we investigate how to avoid side effects in a symbolic planning setting. We study the notion of minimizing side effects in the context of a planning environment where multiple independent agents co-exist. We define (classes of) negative side effects in terms of their effect on the agency of those other agents. Finally, we show how plans which minimize side effects of different types can be computed via compilations to cost-optimizing symbolic planning, and investigate experimentally.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"zhou-muise-hu-permutationinvariantrepresentationofneuralnetworkswithneuronembeddings-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://link.springer.com/chapter/10.1007/978-3-031-02056-8_19\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'zhou-muise-hu-permutationinvariantrepresentationofneuralnetworkswithneuronembeddings-2022', 'https://link.springer.com/chapter/10.1007/978-3-031-02056-8_19', event);\">\n    Permutation-Invariant Representation of Neural Networks with Neuron Embeddings.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Zhou, R.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>;  and Hu, T.\n</span>\n\n  \n\n</span>\n\n  In <i>European Conference on Genetic Programming</i>, pages 294–-308.  2022.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://link.springer.com/chapter/10.1007/978-3-031-02056-8_19\"\n     onclick=\"log_download('zhou-muise-hu-permutationinvariantrepresentationofneuralnetworkswithneuronembeddings-2022', 'https://link.springer.com/chapter/10.1007/978-3-031-02056-8_19', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Permutation-Invariant Representation of Neural Networks with Neuron Embeddings [link]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/zhou-muise-hu-permutationinvariantrepresentationofneuralnetworkswithneuronembeddings-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('zhou-muise-hu-permutationinvariantrepresentationofneuralnetworkswithneuronembeddings-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{zhou-eurogp-2022,\n  title     = {Permutation-Invariant Representation of Neural Networks with Neuron Embeddings},\n  author    = {Ryan Zhou and Christian Muise and Ting Hu},\n  booktitle = {European Conference on Genetic Programming},\n  pages     = {294–-308},\n  year      = {2022},\n  url_paper = {https://link.springer.com/chapter/10.1007/978-3-031-02056-8_19},\n  abstract  = {Neural networks are traditionally represented in terms of their weights. A key property of this representation is that there are multiple representations of a network which can be obtained by permuting the order of the neurons. These representations are generally not compatible between networks, making recombination a challenge for two arbitrary neural networks - an issue known as the “permutation problem” in neuroevolution. This paper proposes an indirect encoding in which a neural network is represented in terms of interactions between neurons rather than explicit weights, and which works for both fully connected and convolutional networks. In addition to reducing the number of free parameters, this encoding is agnostic to the ordering of neurons, bypassing a key problem for direct weight-based representation. This allows us to transplant individual neurons and layers into another network without accounting for the specific ordering of neurons. We show through experiments on the MNIST and CIFAR-10 datasets that this method is capable of representing networks which achieve comparable performance to direct weight representation, and that combining networks this way preserves a larger degree of performance than through direct weight transfer.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Neural networks are traditionally represented in terms of their weights. A key property of this representation is that there are multiple representations of a network which can be obtained by permuting the order of the neurons. These representations are generally not compatible between networks, making recombination a challenge for two arbitrary neural networks - an issue known as the “permutation problem” in neuroevolution. This paper proposes an indirect encoding in which a neural network is represented in terms of interactions between neurons rather than explicit weights, and which works for both fully connected and convolutional networks. In addition to reducing the number of free parameters, this encoding is agnostic to the ordering of neurons, bypassing a key problem for direct weight-based representation. This allows us to transplant individual neurons and layers into another network without accounting for the specific ordering of neurons. We show through experiments on the MNIST and CIFAR-10 datasets that this method is capable of representing networks which achieve comparable performance to direct weight representation, and that combining networks this way preserves a larger degree of performance than through direct weight transfer.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"muise-belle-felli-mcilraith-miller-pearce-sonenberg-efficientmultiagentepistemicplanningteachingplannersaboutnestedbelief-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://arxiv.org/pdf/2110.02480.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'muise-belle-felli-mcilraith-miller-pearce-sonenberg-efficientmultiagentepistemicplanningteachingplannersaboutnestedbelief-2022', 'https://arxiv.org/pdf/2110.02480.pdf', event);\">\n    Efficient Multi-agent Epistemic Planning: Teaching Planners About Nested Belief.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Belle, V.; Felli, P.; McIlraith, S.; Miller, T.; Pearce, A.;  and Sonenberg, L.\n</span>\n\n  \n\n</span>\n\n  In <i>International Conference on Automated Planning and Scheduling: Journal Track</i>.  2022.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://arxiv.org/pdf/2110.02480.pdf\"\n     onclick=\"log_download('muise-belle-felli-mcilraith-miller-pearce-sonenberg-efficientmultiagentepistemicplanningteachingplannersaboutnestedbelief-2022', 'https://arxiv.org/pdf/2110.02480.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Efficient Multi-agent Epistemic Planning: Teaching Planners About Nested Belief [pdf]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-belle-felli-mcilraith-miller-pearce-sonenberg-efficientmultiagentepistemicplanningteachingplannersaboutnestedbelief-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>20 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-belle-felli-mcilraith-miller-pearce-sonenberg-efficientmultiagentepistemicplanningteachingplannersaboutnestedbelief-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{muise-icapsjournal-2022,\n  title     = {Efficient Multi-agent Epistemic Planning: Teaching Planners About Nested Belief},\n  author    = {Christian Muise and Vaishak Belle and Paolo Felli and Sheila McIlraith and Tim Miller and Adrian Pearce and Liz Sonenberg},\n  booktitle = {International Conference on Automated Planning and Scheduling: Journal Track},\n  year      = {2022},\n  url_paper = {https://arxiv.org/pdf/2110.02480.pdf},\n  abstract  = {Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology for solving efficiently. Our approach represents an important step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology for solving efficiently. Our approach represents an important step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"klassen-mcilraith-muise-anaisafetythreatfromlearnedplanningmodels-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://icaps22.icaps-conference.org/workshops/RDDPS/papers/klassen_etal_rddps22.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'klassen-mcilraith-muise-anaisafetythreatfromlearnedplanningmodels-2022', 'http://icaps22.icaps-conference.org/workshops/RDDPS/papers/klassen_etal_rddps22.pdf', event);\">\n    An AI Safety Threat from Learned Planning Models.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Klassen, T. Q.; McIlraith, S.;  and <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>ICAPS Workshop on Reliable Data-Driven Planning and Scheduling</i>.  2022.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://icaps22.icaps-conference.org/workshops/RDDPS/papers/klassen_etal_rddps22.pdf\"\n     onclick=\"log_download('klassen-mcilraith-muise-anaisafetythreatfromlearnedplanningmodels-2022', 'http://icaps22.icaps-conference.org/workshops/RDDPS/papers/klassen_etal_rddps22.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"An AI Safety Threat from Learned Planning Models [pdf]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/klassen-mcilraith-muise-anaisafetythreatfromlearnedplanningmodels-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>38 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('klassen-mcilraith-muise-anaisafetythreatfromlearnedplanningmodels-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{klassen-rddps-2022,\n  title     = {An AI Safety Threat from Learned Planning Models},\n  author    = {Toryn Q. Klassen and Sheila McIlraith and Christian Muise},\n  booktitle = {ICAPS Workshop on Reliable Data-Driven Planning and Scheduling},\n  year      = {2022},\n  url_paper = {http://icaps22.icaps-conference.org/workshops/RDDPS/papers/klassen_etal_rddps22.pdf},\n  abstract  = {Historically, planning problems have often been constructed by hand, with the domain model and the goal developed together, leading to the model and goal being in harmony in the sense that the goal describes exactly which parts of the modelled state were desired to be changed (and not changed) as a consequence of the execution of the plan. With models learned from data, human goal specifiers may not know all the aspects of the model, nor have spent much time thinking about the real world situation that is being modelled. Also, naive users may expect the goals they specify to be interpreted in a commonsensical way by the automated planning system. These things may lead human goal specifiers to more often create incomplete goal specifications, failing to take into account all the different ways the environment can be changed– the potential side effects of plans. This could threaten safety. However, learned models may in some cases also have the feature of having detailed state representations, affording the opportunity for symbolic planning algorithms to recognize side effects that their human users did not think of, and to help avoid them. We propose in this position paper that researchers in symbolic planning should take up the challenge of developing planning algorithms that can safely deal with underspecified objectives– i.e., with problem goals that fail to specify everything that people want.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Historically, planning problems have often been constructed by hand, with the domain model and the goal developed together, leading to the model and goal being in harmony in the sense that the goal describes exactly which parts of the modelled state were desired to be changed (and not changed) as a consequence of the execution of the plan. With models learned from data, human goal specifiers may not know all the aspects of the model, nor have spent much time thinking about the real world situation that is being modelled. Also, naive users may expect the goals they specify to be interpreted in a commonsensical way by the automated planning system. These things may lead human goal specifiers to more often create incomplete goal specifications, failing to take into account all the different ways the environment can be changed– the potential side effects of plans. This could threaten safety. However, learned models may in some cases also have the feature of having detailed state representations, affording the opportunity for symbolic planning algorithms to recognize side effects that their human users did not think of, and to help avoid them. We propose in this position paper that researchers in symbolic planning should take up the challenge of developing planning algorithms that can safely deal with underspecified objectives– i.e., with problem goals that fail to specify everything that people want.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"asai-kajino-fukunaga-muise-symbolicreasoninginlatentspaceclassicalplanningasanexample-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.iospress.com/catalog/books/neuro-symbolic-artificial-intelligence-the-state-of-the-art\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'asai-kajino-fukunaga-muise-symbolicreasoninginlatentspaceclassicalplanningasanexample-2022', 'https://www.iospress.com/catalog/books/neuro-symbolic-artificial-intelligence-the-state-of-the-art', event);\">\n    Symbolic Reasoning in Latent Space: Classical Planning as an Example.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Asai, M.; Kajino, H.; Fukunaga, A.;  and <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>Neuro-Symbolic Artificial Intelligence: The State of the Art</i>, pages 52–77. IOPress,  2022.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.iospress.com/catalog/books/neuro-symbolic-artificial-intelligence-the-state-of-the-art\"\n     onclick=\"log_download('asai-kajino-fukunaga-muise-symbolicreasoninginlatentspaceclassicalplanningasanexample-2022', 'https://www.iospress.com/catalog/books/neuro-symbolic-artificial-intelligence-the-state-of-the-art', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Symbolic Reasoning in Latent Space: Classical Planning as an Example [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/asai-kajino-fukunaga-muise-symbolicreasoninginlatentspaceclassicalplanningasanexample-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>3 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('asai-kajino-fukunaga-muise-symbolicreasoninginlatentspaceclassicalplanningasanexample-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{nesy-book-2022,\n  title={Symbolic Reasoning in Latent Space: Classical Planning as an Example},\n  author={Masataro Asai and Hiroshi Kajino and Alex Fukunaga and Christian Muise},\n  booktitle={Neuro-Symbolic Artificial Intelligence: The State of the Art},\n  pages={52--77},\n  year={2022},\n  publisher={IOPress},\n  url={https://www.iospress.com/catalog/books/neuro-symbolic-artificial-intelligence-the-state-of-the-art},\n  abstract={Symbolic systems require hand-coded symbolic representation as input, resulting in a knowledge acquisition bottleneck. Meanwhile, although deep learning has achieved significant success in many fields, the knowledge is encoded in a subsymbolic representation which is incompatible with symbolic systems. To address the gap between the two fields, one has to solve Symbol Grounding problem: The question of how a machine can generate symbols automatically. We discuss our recent work called Latplan, an unsupervised architecture combining deep learning and classical planning. Given only an unlabeled set of image pairs showing a subset of transitions allowed in the environment (training inputs), Latplan learns a complete propositional PDDL action model of the environment. Later, when a pair of images representing the initial and the goal states (planning inputs) is given, Latplan finds a plan to the goal state in a symbolic latent space and returns a visualized plan execution. We discuss several key ideas that made Latplan possible which would hopefully extend to many other symbolic paradigms outside classical planning.}\n}\n\n\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Symbolic systems require hand-coded symbolic representation as input, resulting in a knowledge acquisition bottleneck. Meanwhile, although deep learning has achieved significant success in many fields, the knowledge is encoded in a subsymbolic representation which is incompatible with symbolic systems. To address the gap between the two fields, one has to solve Symbol Grounding problem: The question of how a machine can generate symbols automatically. We discuss our recent work called Latplan, an unsupervised architecture combining deep learning and classical planning. Given only an unlabeled set of image pairs showing a subset of transitions allowed in the environment (training inputs), Latplan learns a complete propositional PDDL action model of the environment. Later, when a pair of images representing the initial and the goal states (planning inputs) is given, Latplan finds a plan to the goal state in a symbolic latent space and returns a visualized plan execution. We discuss several key ideas that made Latplan possible which would hopefully extend to many other symbolic paradigms outside classical planning.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2021\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2021')\"\n      onkeypress=\"toggleGroup('group_2021')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2021</span>\n      <span class=\"bibbase_group_count\">\n        \n        (7)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"liu-muise-aneuralsymbolicapproachforobjectnavigation-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://embodied-ai.org/papers/A-Neural-Symbolic-Approach-for-Object-Navigation.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'liu-muise-aneuralsymbolicapproachforobjectnavigation-2021', 'https://embodied-ai.org/papers/A-Neural-Symbolic-Approach-for-Object-Navigation.pdf', event);\">\n    A Neural-Symbolic Approach for Object Navigation.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Liu, X.;  and <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>2nd Embodied AI Workshop (CVPR 2021)</i>,  2021. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://embodied-ai.org/papers/A-Neural-Symbolic-Approach-for-Object-Navigation.pdf\"\n     onclick=\"log_download('liu-muise-aneuralsymbolicapproachforobjectnavigation-2021', 'https://embodied-ai.org/papers/A-Neural-Symbolic-Approach-for-Object-Navigation.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"A Neural-Symbolic Approach for Object Navigation [pdf]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n  <a href=\"https://embodied-ai.org/posters/Neural-Symbolic-Approach-for-Object-Navigation.pdf\"\n     onclick=\"log_download('liu-muise-aneuralsymbolicapproachforobjectnavigation-2021', 'https://embodied-ai.org/posters/Neural-Symbolic-Approach-for-Object-Navigation.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"A Neural-Symbolic Approach for Object Navigation [pdf]\"\n       title=\" poster\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> poster</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/liu-muise-aneuralsymbolicapproachforobjectnavigation-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('liu-muise-aneuralsymbolicapproachforobjectnavigation-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{liu-eai-2021,\n  author    = {Xiaotian Liu and Christian Muise},\n  title     = {A Neural-Symbolic Approach for Object Navigation},\n  booktitle = {2nd Embodied AI Workshop (CVPR 2021)},\n  year      = {2021},\n  url_paper = {https://embodied-ai.org/papers/A-Neural-Symbolic-Approach-for-Object-Navigation.pdf},\n  url_poster = {https://embodied-ai.org/posters/Neural-Symbolic-Approach-for-Object-Navigation.pdf},\n  abstract  = {Object navigation refers to the task of discovering and locating objects in an unknown environment. End-to-end deep learning methods struggle at this task due to sparse rewards. In this work, we propose a simple neural-symbolic approach for object navigation in the AI2-THOR environment. Our method takes raw RGB images as input and uses a spatial memory graph as memory to store object and location information. The architecture consists of both a convolutional neural network for object detection and a spatial graph to represent the environment. By having a discrete graph representation of the environment, the agent can directly use search or planning algorithms as high-level reasoning engines. Model performance is evaluated on both task completion rate and steps required to reach target objects. Empirical results demonstrate that our approach can achieve performance close to the optimal. Our work builds a foundation for a neural-symbolic approach that can reason via unstructured visual cues.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Object navigation refers to the task of discovering and locating objects in an unknown environment. End-to-end deep learning methods struggle at this task due to sparse rewards. In this work, we propose a simple neural-symbolic approach for object navigation in the AI2-THOR environment. Our method takes raw RGB images as input and uses a spatial memory graph as memory to store object and location information. The architecture consists of both a convolutional neural network for object detection and a spatial graph to represent the environment. By having a discrete graph representation of the environment, the agent can directly use search or planning algorithms as high-level reasoning engines. Model performance is evaluated on both task completion rate and steps required to reach target objects. Empirical results demonstrate that our approach can achieve performance close to the optimal. Our work builds a foundation for a neural-symbolic approach that can reason via unstructured visual cues.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"liu-armstrong-nabil-muise-exploringmultiviewperspectivesondeepreinforcementlearningagentsforembodiedobjectnavigationinvirtualhomeenvironments-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://mulab.ai/papers/2021-cascon-liu-armstrong.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'liu-armstrong-nabil-muise-exploringmultiviewperspectivesondeepreinforcementlearningagentsforembodiedobjectnavigationinvirtualhomeenvironments-2021', 'https://mulab.ai/papers/2021-cascon-liu-armstrong.pdf', event);\">\n    Exploring Multi-View Perspectives on Deep Reinforcement Learning Agents for Embodied Object Navigation in Virtual Home Environments.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Liu, X.; Armstrong, V.; Nabil, S.;  and <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>CASCON EVOKE</i>,  2021. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://mulab.ai/papers/2021-cascon-liu-armstrong.pdf\"\n     onclick=\"log_download('liu-armstrong-nabil-muise-exploringmultiviewperspectivesondeepreinforcementlearningagentsforembodiedobjectnavigationinvirtualhomeenvironments-2021', 'https://mulab.ai/papers/2021-cascon-liu-armstrong.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Exploring Multi-View Perspectives on Deep Reinforcement Learning Agents for Embodied Object Navigation in Virtual Home Environments [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/liu-armstrong-nabil-muise-exploringmultiviewperspectivesondeepreinforcementlearningagentsforembodiedobjectnavigationinvirtualhomeenvironments-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('liu-armstrong-nabil-muise-exploringmultiviewperspectivesondeepreinforcementlearningagentsforembodiedobjectnavigationinvirtualhomeenvironments-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{liu-cascon-2021,\n  author    = {Xiaotian Liu and Victoria Armstrong and Sara Nabil and Christian Muise},\n  title     = {Exploring Multi-View Perspectives on Deep Reinforcement Learning Agents for Embodied Object Navigation in Virtual Home Environments},\n  booktitle = {CASCON EVOKE},\n  year      = {2021},\n  url       = {https://mulab.ai/papers/2021-cascon-liu-armstrong.pdf},\n  abstract  = {Recent years have brought the exploration of embodied reinforcement learning agents in a variety of domains. One of the advantages of artificial agents is that they can obtain visual inputs simultaneously using multiple input devices. This work explores multi-view reinforcement learning for object navigation tasks in 3D rendered virtual home environments using AI2-THOR. We trained CNN based Deep Q-learning embodied agents with egocentric, allocentric, and combined egocentric-allocentric perspectives to locate an object in an unknown environment. We compared the results of the three RL agents, and evaluated them by both reward improvement rate, and reward obtained. We demonstrate that the egocentric perspective allows for faster reward accumulation in the earlier episodes, whereas the allocentric agents obtained better long-term rewards. Interesting results arise from the combined allocentric and egocentric perspective, where we found that the agent had the best overall results by harnessing the benefits of each perspective. The results show that while single perspective embodied agents each have their own advantages, combining both inputs yield the best overall reward. Our findings provide a foundation and benchmark for building embodied RL agents with multi-view perspectives.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Recent years have brought the exploration of embodied reinforcement learning agents in a variety of domains. One of the advantages of artificial agents is that they can obtain visual inputs simultaneously using multiple input devices. This work explores multi-view reinforcement learning for object navigation tasks in 3D rendered virtual home environments using AI2-THOR. We trained CNN based Deep Q-learning embodied agents with egocentric, allocentric, and combined egocentric-allocentric perspectives to locate an object in an unknown environment. We compared the results of the three RL agents, and evaluated them by both reward improvement rate, and reward obtained. We demonstrate that the egocentric perspective allows for faster reward accumulation in the earlier episodes, whereas the allocentric agents obtained better long-term rewards. Interesting results arise from the combined allocentric and egocentric perspective, where we found that the agent had the best overall results by harnessing the benefits of each perspective. The results show that while single perspective embodied agents each have their own advantages, combining both inputs yield the best overall reward. Our findings provide a foundation and benchmark for building embodied RL agents with multi-view perspectives.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"simon-muise-anaturallanguagemodelforgeneratingpddl-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://icaps21.icaps-conference.org/workshops/KEPS/Papers/KEPS_2021_paper_7.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'simon-muise-anaturallanguagemodelforgeneratingpddl-2021', 'https://icaps21.icaps-conference.org/workshops/KEPS/Papers/KEPS_2021_paper_7.pdf', event);\">\n    A Natural Language Model for Generating PDDL.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Simon, N.;  and <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>The ICAPS Workshop on Knowledge Engineering for Planning and Scheduling (KEPS)</i>,  2021. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://icaps21.icaps-conference.org/workshops/KEPS/Papers/KEPS_2021_paper_7.pdf\"\n     onclick=\"log_download('simon-muise-anaturallanguagemodelforgeneratingpddl-2021', 'https://icaps21.icaps-conference.org/workshops/KEPS/Papers/KEPS_2021_paper_7.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"A Natural Language Model for Generating PDDL [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/simon-muise-anaturallanguagemodelforgeneratingpddl-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('simon-muise-anaturallanguagemodelforgeneratingpddl-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{simon-keps-2021,\n  author    = {Nisha Simon and Christian Muise},\n  title     = {A Natural Language Model for Generating PDDL},\n  booktitle = {The ICAPS Workshop on Knowledge Engineering for Planning and Scheduling (KEPS)},\n  year      = {2021},\n  url       = {https://icaps21.icaps-conference.org/workshops/KEPS/Papers/KEPS_2021_paper_7.pdf},\n  abstract  = {Language generation in various domains has drawn a large amount of interest in recent years. This paper studies language generation in the context of generating planning specifications in the syntax typically used for this task: the Planning Domain Definition Language (PDDL). The goal of this preliminary work is to predict the next completion in PDDL code, based on previous and surrounding text. Generating valid PDDL code is a key component in creating robust planners. Thus, the ability to generate PDDL code will be extremely useful to PDDL practitioners for the purpose of solving planning problems. It further opens the door to providing a source of inspiration for the modeller. The main contribution of our approach is a language model built using Recurrent Neural Networks (RNNs) that is trained on existing PDDL domains, which can be used to generate PDDL-like code. We train our model on a corpus of publicly available PDDL files from api.planning.domains, and evaluate our approach in the setting of PDDL auto-prediction for some of the more common domains. We found that code-like generation is possible, although fluency can be improved.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Language generation in various domains has drawn a large amount of interest in recent years. This paper studies language generation in the context of generating planning specifications in the syntax typically used for this task: the Planning Domain Definition Language (PDDL). The goal of this preliminary work is to predict the next completion in PDDL code, based on previous and surrounding text. Generating valid PDDL code is a key component in creating robust planners. Thus, the ability to generate PDDL code will be extremely useful to PDDL practitioners for the purpose of solving planning problems. It further opens the door to providing a source of inspiration for the modeller. The main contribution of our approach is a language model built using Recurrent Neural Networks (RNNs) that is trained on existing PDDL domains, which can be used to generate PDDL-like code. We train our model on a corpus of publicly available PDDL files from api.planning.domains, and evaluate our approach in the setting of PDDL auto-prediction for some of the more common domains. We found that code-like generation is possible, although fluency can be improved.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"aspinall-cunningham-sekine-muise-aplanningdomainspluginforheuristicvisualization-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://icaps21.icaps-conference.org/demos/demos/377.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'aspinall-cunningham-sekine-muise-aplanningdomainspluginforheuristicvisualization-2021', 'https://icaps21.icaps-conference.org/demos/demos/377.pdf', event);\">\n    A Planning.Domains Plugin for Heuristic Visualization.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Aspinall, C.; Cunningham, C.; Sekine, E.;  and <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>System Demonstrations at the Thirty-First International Conference on Automated Planning and Scheduling (ICAPS)</i>,  2021. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://icaps21.icaps-conference.org/demos/demos/377.pdf\"\n     onclick=\"log_download('aspinall-cunningham-sekine-muise-aplanningdomainspluginforheuristicvisualization-2021', 'https://icaps21.icaps-conference.org/demos/demos/377.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"A Planning.Domains Plugin for Heuristic Visualization [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/aspinall-cunningham-sekine-muise-aplanningdomainspluginforheuristicvisualization-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('aspinall-cunningham-sekine-muise-aplanningdomainspluginforheuristicvisualization-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{heurviz-icaps21demo,\n  author    = {Caitlin Aspinall and Cam Cunningham and Ellie Sekine and Christian Muise},\n  title     = {A Planning.Domains Plugin for Heuristic Visualization},\n  booktitle = {System Demonstrations at the Thirty-First International Conference on Automated Planning and Scheduling ({ICAPS})},\n  year      = {2021},\n  url       = {https://icaps21.icaps-conference.org/demos/demos/377.pdf},\n  abstract  = {Heuristics are at the heart of every planner. From the simple to the complex, they drive both satisficing and optimal planning approaches. Despite their pervasiveness, there has been relatively little effort towards systematically visualizing them. We introduce a plugin for the online editor at Planning.Domains that is capable of visualizing the heuristic computation of a manually explored state space. Our initial implementation demonstrates the hadd heuristic, but the framework serves as an extensible base for other heuristics in the field of automated planning.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Heuristics are at the heart of every planner. From the simple to the complex, they drive both satisficing and optimal planning approaches. Despite their pervasiveness, there has been relatively little effort towards systematically visualizing them. We introduce a plugin for the online editor at Planning.Domains that is capable of visualizing the heuristic computation of a manually explored state space. Our initial implementation demonstrates the hadd heuristic, but the framework serves as an extensible base for other heuristics in the field of automated planning.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"liu-paredes-muise-doyouseewhatiseeanegocentricviewofourpansophicalplanningproblems-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://icaps21.icaps-conference.org/workshops/IntEx/IntEx2021proceedings_final.pdf#page=36\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'liu-paredes-muise-doyouseewhatiseeanegocentricviewofourpansophicalplanningproblems-2021', 'https://icaps21.icaps-conference.org/workshops/IntEx/IntEx2021proceedings_final.pdf#page=36', event);\">\n    Do You See What I See? An Egocentric View of our Pansophical Planning Problems.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Liu, X.; Paredes, A.;  and <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>The ICAPS Workshop on Integrated Planning, Acting, and Execution (IntEx)</i>,  2021. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://icaps21.icaps-conference.org/workshops/IntEx/IntEx2021proceedings_final.pdf#page=36\"\n     onclick=\"log_download('liu-paredes-muise-doyouseewhatiseeanegocentricviewofourpansophicalplanningproblems-2021', 'https://icaps21.icaps-conference.org/workshops/IntEx/IntEx2021proceedings_final.pdf#page=36', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Do You See What I See? An Egocentric View of our Pansophical Planning Problems [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/liu-paredes-muise-doyouseewhatiseeanegocentricviewofourpansophicalplanningproblems-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('liu-paredes-muise-doyouseewhatiseeanegocentricviewofourpansophicalplanningproblems-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{liu2021intex,\n  author    = {Xiaotian Liu and Alison Paredes and Christian Muise},\n  title     = {Do You See What I See? An Egocentric View of our Pansophical Planning Problems},\n  booktitle = {The ICAPS Workshop on Integrated Planning, Acting, and Execution (IntEx)},\n  url       = {https://icaps21.icaps-conference.org/workshops/IntEx/IntEx2021proceedings_final.pdf#page=36},\n  abstract  = {Classical planning takes a pansophical view of the world: everything is fully known, observed, and static. While there are extensions to partial observability, this leapfrogs an important intermediate step of embodied agent design: egocentricity. In this work, we propose a semi-automated mechanism that allows planning domain designers to convert classical planning problems into an egocentric alternative. The generated planning problems are classical as well, and we introduce an open-loop replanning mechanism that progressively explores the egocentric space until the original goal is solved (or deemed unsolvable). Our work serves as a crucial first step towards embodied agents that can be equipped with an appropriately specified egocentric version of known environment dynamics.},\n  year      = {2021},\n}\n\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Classical planning takes a pansophical view of the world: everything is fully known, observed, and static. While there are extensions to partial observability, this leapfrogs an important intermediate step of embodied agent design: egocentricity. In this work, we propose a semi-automated mechanism that allows planning domain designers to convert classical planning problems into an egocentric alternative. The generated planning problems are classical as well, and we introduce an open-loop replanning mechanism that progressively explores the egocentric space until the original goal is solved (or deemed unsolvable). Our work serves as a crucial first step towards embodied agents that can be equipped with an appropriately specified egocentric version of known environment dynamics.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"muise-belle-felli-mcilraith-miller-pearce-sonenberg-efficientmultiagentepistemicplanningteachingplannersaboutnestedbelief-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://arxiv.org/pdf/2110.02480.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'muise-belle-felli-mcilraith-miller-pearce-sonenberg-efficientmultiagentepistemicplanningteachingplannersaboutnestedbelief-2021', 'https://arxiv.org/pdf/2110.02480.pdf', event);\">\n    Efficient Multi-agent Epistemic Planning: Teaching Planners About Nested Belief.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Belle, V.; Felli, P.; McIlraith, S. A.; Miller, T.; Pearce, A. R.;  and Sonenberg, L.\n</span>\n\n  \n\n</span>\n\n  <i>Artificial Intelligence Journal</i>.  2021.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://arxiv.org/pdf/2110.02480.pdf\"\n     onclick=\"log_download('muise-belle-felli-mcilraith-miller-pearce-sonenberg-efficientmultiagentepistemicplanningteachingplannersaboutnestedbelief-2021', 'https://arxiv.org/pdf/2110.02480.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Efficient Multi-agent Epistemic Planning: Teaching Planners About Nested Belief [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-belle-felli-mcilraith-miller-pearce-sonenberg-efficientmultiagentepistemicplanningteachingplannersaboutnestedbelief-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>31 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-belle-felli-mcilraith-miller-pearce-sonenberg-efficientmultiagentepistemicplanningteachingplannersaboutnestedbelief-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{muise2021aij,\n  author    = {Christian Muise and Vaishak Belle and Paolo Felli and Sheila A. McIlraith and Tim Miller and Adrian R. Pearce and Liz Sonenberg},\n  title     = {Efficient Multi-agent Epistemic Planning: Teaching Planners About Nested Belief},\n  journal   = {Artificial Intelligence Journal},\n  year      = {2021},\n  url       = {https://arxiv.org/pdf/2110.02480.pdf},\n  abstract  = {Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology for solving efficiently. Our approach represents an important step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology for solving efficiently. Our approach represents an important step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"muise-lipovetzky-kepsbookplanningdomains-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  KEPS Book: Planning. Domains.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>;  and Lipovetzky, N.\n</span>\n\n  \n\n</span>\n\n  In <i>Knowledge Engineering Tools and Techniques for AI Planning</i>, pages 91–105. Springer,  2021.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-lipovetzky-kepsbookplanningdomains-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-lipovetzky-kepsbookplanningdomains-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{muise2021keps,\n  title={KEPS Book: Planning. Domains},\n  author={Muise, Christian and Lipovetzky, Nir},\n  booktitle={Knowledge Engineering Tools and Techniques for AI Planning},\n  pages={91--105},\n  year={2021},\n  publisher={Springer}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2020\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2020')\"\n      onkeypress=\"toggleGroup('group_2020')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2020</span>\n      <span class=\"bibbase_group_count\">\n        \n        (7)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"zhang-muise-actionusabilityviadeadenddetection-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Action Usability via Deadend Detection.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Zhang, Q.;  and <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>The ICAPS Workshop on Knowledge Engineering for Planning and Scheduling (KEPS)</i>,  2020. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/zhang-muise-actionusabilityviadeadenddetection-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('zhang-muise-actionusabilityviadeadenddetection-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{zhang-keps-2020,\n  author    = {Qianyu Zhang and Christian Muise},\n  title     = {Action Usability via Deadend Detection},\n  booktitle = {The ICAPS Workshop on Knowledge Engineering for Planning and Scheduling (KEPS)},\n  year      = {2020},\n  abstract  = {The vast majority of planning problem models are incorrect, incomplete, or simply inconsistent. This is particularly an issue during the development process of a planning model. However, there are few existing debugging tools for modeling to aid with this issue. In this paper, we introduce a method for detecting actions that are deemed unusable, which may naturally be a result of modelling errors. We use action usability (or reachability) detection for on-the-fly diagnostics of planning models. In our technique, each action is checked for usability via problem reformulation and unsolvability detection. Through the analysis of usability, this technique could improve the modeling process directly, and we have demonstrated this capability through the tight integration with the online PDDL editor at Planning.Domains.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The vast majority of planning problem models are incorrect, incomplete, or simply inconsistent. This is particularly an issue during the development process of a planning model. However, there are few existing debugging tools for modeling to aid with this issue. In this paper, we introduce a method for detecting actions that are deemed unusable, which may naturally be a result of modelling errors. We use action usability (or reachability) detection for on-the-fly diagnostics of planning models. In our technique, each action is checked for usability via problem reformulation and unsolvability detection. Through the analysis of usability, this technique could improve the modeling process directly, and we have demonstrated this capability through the tight integration with the online PDDL editor at Planning.Domains.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"wollensteinbetech-muise-cassandrass-paschalidis-khazaeni-explainabilityofintelligenttransportationsystemsusingknowledgecompilationatrafficlightcontrollercase-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://arxiv.org/pdf/2007.04916.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'wollensteinbetech-muise-cassandrass-paschalidis-khazaeni-explainabilityofintelligenttransportationsystemsusingknowledgecompilationatrafficlightcontrollercase-2020', 'https://arxiv.org/pdf/2007.04916.pdf', event);\">\n    Explainability of Intelligent Transportation Systems using Knowledge Compilation: a Traffic Light Controller Case.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Wollenstein-Betech, S.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Cassandrass, C. G.; Paschalidis, I. C.;  and Khazaeni, Y.\n</span>\n\n  \n\n</span>\n\n  In <i>The 23rd IEEE International Conference on Intelligent Transportation Systems</i>,  2020. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://arxiv.org/pdf/2007.04916.pdf\"\n     onclick=\"log_download('wollensteinbetech-muise-cassandrass-paschalidis-khazaeni-explainabilityofintelligenttransportationsystemsusingknowledgecompilationatrafficlightcontrollercase-2020', 'https://arxiv.org/pdf/2007.04916.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Explainability of Intelligent Transportation Systems using Knowledge Compilation: a Traffic Light Controller Case [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/wollensteinbetech-muise-cassandrass-paschalidis-khazaeni-explainabilityofintelligenttransportationsystemsusingknowledgecompilationatrafficlightcontrollercase-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>11 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('wollensteinbetech-muise-cassandrass-paschalidis-khazaeni-explainabilityofintelligenttransportationsystemsusingknowledgecompilationatrafficlightcontrollercase-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{betech-itsc-2020,\n  author = {Salomon Wollenstein-Betech and Christian Muise and Christos G. Cassandrass and Ioannis Ch. Paschalidis and Yasaman Khazaeni},\n  title = {Explainability of Intelligent Transportation Systems using Knowledge Compilation: a Traffic Light Controller Case},\n  booktitle = {The 23rd IEEE International Conference on Intelligent Transportation Systems},\n  year = {2020},\n  url = {https://arxiv.org/pdf/2007.04916.pdf},\n  abstract = {Usage of automated controllers which make decisions on an environment are widespread and are often based on black-box models. We use Knowledge Compilation theory to bring explainability to the controllers decision given the state of the system. For this, we use simulated historical state action data as input and build a compact and structured representation which relates states with actions. We implement this method in a Traffic Light Control scenario where the controller selects the light cycle by observing the presence (or absence) of vehicles in different regions of the incoming roads.},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Usage of automated controllers which make decisions on an environment are widespread and are often based on black-box models. We use Knowledge Compilation theory to bring explainability to the controllers decision given the state of the system. For this, we use simulated historical state action data as input and build a compact and structured representation which relates states with actions. We implement this method in a Traffic Light Control scenario where the controller selects the light cycle by observing the presence (or absence) of vehicles in different regions of the incoming roads.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"asai-muise-learningneuralsymbolicdescriptiveplanningmodelsviacubespacepriorsthevoyagehometostrips-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://arxiv.org/pdf/2004.12850.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'asai-muise-learningneuralsymbolicdescriptiveplanningmodelsviacubespacepriorsthevoyagehometostrips-2020', 'https://arxiv.org/pdf/2004.12850.pdf', event);\">\n    Learning Neural-Symbolic Descriptive Planning Models via Cube-Space Priors: The Voyage Home (to STRIPS).\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Asai, M.;  and <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>29th International Joint Conference on Artificial Intelligence</i>,  2020. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://arxiv.org/pdf/2004.12850.pdf\"\n     onclick=\"log_download('asai-muise-learningneuralsymbolicdescriptiveplanningmodelsviacubespacepriorsthevoyagehometostrips-2020', 'https://arxiv.org/pdf/2004.12850.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Learning Neural-Symbolic Descriptive Planning Models via Cube-Space Priors: The Voyage Home (to STRIPS) [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/asai-muise-learningneuralsymbolicdescriptiveplanningmodelsviacubespacepriorsthevoyagehometostrips-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>21 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('asai-muise-learningneuralsymbolicdescriptiveplanningmodelsviacubespacepriorsthevoyagehometostrips-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{asai-ijcai-2020,\n  author = {Masataro Asai and Christian Muise},\n  title = {Learning Neural-Symbolic Descriptive Planning Models via Cube-Space Priors: The Voyage Home (to STRIPS)},\n  booktitle = {29th International Joint Conference on Artificial Intelligence},\n  url = {https://arxiv.org/pdf/2004.12850.pdf},\n  abstract = {We achieved a new milestone in the difficult task of enabling agents to learn about their environment autonomously. Our neuro-symbolic architecture is trained end-to-end to produce a succinct and effective discrete state transition model from images alone. Our target representation (the Planning Domain Definition Language) is already in a form that off-the-shelf solvers can consume, and opens the door to the rich array of modern heuristic search capabilities. We demonstrate how the sophisticated innate prior we place on the learning process significantly reduces the complexity of the learned representation, and reveals a connection to the graphtheoretic notion of cube-like graphs, thus opening the door to a deeper understanding of the ideal properties for learned symbolic representations. We show that the powerful domain-independent heuristics allow our system to solve visual 15-Puzzle instances which are beyond the reach of blind search, without resorting to the Reinforcement Learning approach that requires a huge amount of training on the domain-dependent reward information.},\n  year = {2020},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  We achieved a new milestone in the difficult task of enabling agents to learn about their environment autonomously. Our neuro-symbolic architecture is trained end-to-end to produce a succinct and effective discrete state transition model from images alone. Our target representation (the Planning Domain Definition Language) is already in a form that off-the-shelf solvers can consume, and opens the door to the rich array of modern heuristic search capabilities. We demonstrate how the sophisticated innate prior we place on the learning process significantly reduces the complexity of the learned representation, and reveals a connection to the graphtheoretic notion of cube-like graphs, thus opening the door to a deeper understanding of the ideal properties for learned symbolic representations. We show that the powerful domain-independent heuristics allow our system to solve visual 15-Puzzle instances which are beyond the reach of blind search, without resorting to the Reinforcement Learning approach that requires a huge amount of training on the domain-dependent reward information.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"sreedharan-chakraborti-muise-khazaeni-kambhampati-d3waacasestudyofxaipinamodelacquisitiontask-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  D3WA+: A Case Study of XAIP in a Model Acquisition Task.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Sreedharan, S.; Chakraborti, T.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Khazaeni, Y.;  and Kambhampati, S.\n</span>\n\n  \n\n</span>\n\n  In <i>Proceedings of the Thirtieth International Conference on Automated Planning and Scheduling (ICAPS)</i>,  2020. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/sreedharan-chakraborti-muise-khazaeni-kambhampati-d3waacasestudyofxaipinamodelacquisitiontask-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('sreedharan-chakraborti-muise-khazaeni-kambhampati-d3waacasestudyofxaipinamodelacquisitiontask-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{sreedharan-icaps20,\n  author    = {Sarath Sreedharan and Tathagata Chakraborti and Christian Muise and Yasaman Khazaeni and Subbarao Kambhampati},\n  title     = {{D3WA+}: A Case Study of {XAIP} in a Model Acquisition Task},\n  booktitle = {Proceedings of the Thirtieth International Conference on Automated Planning and Scheduling ({ICAPS})},\n  year      = {2020},\n  abstract  = {Recently, the D3WA system was proposed as a paradigm shift in how complex goal-oriented dialogue agents can be specified by taking a declarative view of design. However, it turns out actual users of the system have a hard time evolving their mental model and grasping the imperative consequences of declarative design. In this paper, we adopt ideas from existing works in the field of Explainable AI Planning (XAIP) to provide guidance to the dialogue designer during the model acquisition process. We will highlight in the course of this discussion how the setting presents unique challenges to the XAIP setting, including having to deal with a different user persona as the domain modeler rather than the end-user of the system, and consequently having to deal with the unsolvability of models in addition to explaining generated plans.}\n}\n\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Recently, the D3WA system was proposed as a paradigm shift in how complex goal-oriented dialogue agents can be specified by taking a declarative view of design. However, it turns out actual users of the system have a hard time evolving their mental model and grasping the imperative consequences of declarative design. In this paper, we adopt ideas from existing works in the field of Explainable AI Planning (XAIP) to provide guidance to the dialogue designer during the model acquisition process. We will highlight in the course of this discussion how the setting presents unique challenges to the XAIP setting, including having to deal with a different user persona as the domain modeler rather than the end-user of the system, and consequently having to deal with the unsolvability of models in addition to explaining generated plans.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"muise-wollensteinbetech-booth-shah-khazaeni-modelingblackboxagentbehaviourviaknowledgecompilation-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Modeling Blackbox Agent Behaviour via Knowledge Compilation.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Wollenstein-Betech, S.; Booth, S.; Shah, J.;  and Khazaeni, Y.\n</span>\n\n  \n\n</span>\n\n  In <i>The AAAI 2020 Workshop on Plan, Activity, and Intent Recognition</i>,  2020. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-wollensteinbetech-booth-shah-khazaeni-modelingblackboxagentbehaviourviaknowledgecompilation-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-wollensteinbetech-booth-shah-khazaeni-modelingblackboxagentbehaviourviaknowledgecompilation-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{muise-pair20,\n  title={Modeling Blackbox Agent Behaviour via Knowledge Compilation},\n  author={Christian Muise and Salomón Wollenstein-Betech and Serena Booth and Julie Shah and Yasaman Khazaeni},\n  booktitle={The AAAI 2020 Workshop on Plan, Activity, and Intent Recognition},\n  year={2020},\n  abstract={Understanding how agents behave in an environment is a corner-stone for interpretable AI. In this work, we focus on capturing the policy an agent is following without placing any assumptions on how that policy is actually implemented. From a corpus of state-action pairs, our task is to build a compact and diagnosable representation of the mapping from states to actions. We appeal to modern knowledge compilation techniques for this task and demonstrate empirically how this approach outperforms the previous state of the art. We further create an interactive inference on the compiled representation to get an intuitive sense of the policy.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Understanding how agents behave in an environment is a corner-stone for interpretable AI. In this work, we focus on capturing the policy an agent is following without placing any assumptions on how that policy is actually implemented. From a corpus of state-action pairs, our task is to build a compact and diagnosable representation of the mapping from states to actions. We appeal to modern knowledge compilation techniques for this task and demonstrate empirically how this approach outperforms the previous state of the art. We further create an interactive inference on the compiled representation to get an intuitive sense of the policy.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"sreedharan-chakraborti-muise-kambhampati-expectationawareplanningageneralframeworkforsynthesizingandexecutingselfexplainingplansforhumanaiinteraction-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Expectation-Aware Planning: A General Framework for Synthesizing and Executing Self-Explaining Plans for Human-AI Interaction.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Sreedharan, S.; Chakraborti, T.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>;  and Kambhampati, S.\n</span>\n\n  \n\n</span>\n\n  In <i>The 34th AAAI Conference on Artificial Intelligence</i>,  2020. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/sreedharan-chakraborti-muise-kambhampati-expectationawareplanningageneralframeworkforsynthesizingandexecutingselfexplainingplansforhumanaiinteraction-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('sreedharan-chakraborti-muise-kambhampati-expectationawareplanningageneralframeworkforsynthesizingandexecutingselfexplainingplansforhumanaiinteraction-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{sreedharan-aaai20,\n  title={Expectation-Aware Planning: A General Framework for Synthesizing and Executing Self-Explaining Plans for Human-AI Interaction},\n  author={Sarath Sreedharan and Tathagata Chakraborti and Christian Muise and Subbarao Kambhampati},\n  booktitle={The 34th AAAI Conference on Artificial Intelligence},\n  year={2020},\n  abstract={In this work, we present a new planning formalism called Expectation-Aware planning for decision making with humans in the loop where the human&#x27;s expectations about an agent may differ from the agent&#x27;s own model. We show how this formulation allows agents to not only leverage existing strategies for handling model differences like explanations and explicability, but can also exhibit novel behaviors that are generated through the combination of these different strategies. Our formulation also reveals a deep connection to existing approaches in epistemic planning. Specifically, we show how we can leverage classical planning compilations for epistemic planning to solve Expectation-Aware planning problems. To the best of our knowledge, the proposed formulation is the first complete solution to decision-making in the presence of diverging user expectations that is amenable to a classical planning compilation while successfully combining previous works on explanation and explicability. We empirically show how our approach provides a computational advantage over our earlier approaches that rely on search in the space of models.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  In this work, we present a new planning formalism called Expectation-Aware planning for decision making with humans in the loop where the human's expectations about an agent may differ from the agent's own model. We show how this formulation allows agents to not only leverage existing strategies for handling model differences like explanations and explicability, but can also exhibit novel behaviors that are generated through the combination of these different strategies. Our formulation also reveals a deep connection to existing approaches in epistemic planning. Specifically, we show how we can leverage classical planning compilations for epistemic planning to solve Expectation-Aware planning problems. To the best of our knowledge, the proposed formulation is the first complete solution to decision-making in the presence of diverging user expectations that is amenable to a classical planning compilation while successfully combining previous works on explanation and explicability. We empirically show how our approach provides a computational advantage over our earlier approaches that rely on search in the space of models.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"agarwal-muise-agarwal-upadhyay-tang-zeng-khazaeni-tracehubaplatformtobridgethegapbetweenstateofthearttimeseriesanalyticsanddatasets-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  TraceHub – A Platform to bridge the gap between State-Of-The-Art Time-Series Analytics and Datasets.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Agarwal, S.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Agarwal, M.; Upadhyay, S.; Tang, Z.; Zeng, Z.;  and Khazaeni, Y.\n</span>\n\n  \n\n</span>\n\n  In <i>AAAI 2020 System Demonstration Track</i>,  2020. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"><span style=\"color: purple\">Best System Demonstration Award.</span></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/agarwal-muise-agarwal-upadhyay-tang-zeng-khazaeni-tracehubaplatformtobridgethegapbetweenstateofthearttimeseriesanalyticsanddatasets-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('agarwal-muise-agarwal-upadhyay-tang-zeng-khazaeni-tracehubaplatformtobridgethegapbetweenstateofthearttimeseriesanalyticsanddatasets-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{agarwal-aaaidemo20,\n  author={Shubham Agarwal and Christian Muise and Mayank Agarwal and Sohini Upadhyay and Zilu Tang and Zhongshen Zeng and Yasaman Khazaeni},\n  title={TraceHub -- A Platform to bridge the gap between State-Of-The-Art Time-Series Analytics and Datasets},\n  booktitle={AAAI 2020 System Demonstration Track},\n  year={2020},\n  bibbase_note = {&lt;span style=&quot;color: purple&quot;&gt;Best System Demonstration Award.&lt;/span&gt;},\n  abstract={In this paper, we present TraceHub - a platform that connects new non-trivial state-of-the-art time-series analytics with datasets from different domains. Analytics owners can run their insights on new datasets in an automated setting to find insight&#x27;s potential and improve it. Dataset owners can find all possible types of non-trivial insights based on latest research. We provide a plug-n-play system as a set of Dataset, Transformer pipeline, and Analytics APIs for both kinds of users. We show a usefulness measure of generated insights across various types of analytics in the system. We believe that this platform can be used to bridge the gap between time-series analytics and datasets by significantly reducing the time to find the true potential of budding time-series research and improving on it faster.}\n}\n\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  In this paper, we present TraceHub - a platform that connects new non-trivial state-of-the-art time-series analytics with datasets from different domains. Analytics owners can run their insights on new datasets in an automated setting to find insight's potential and improve it. Dataset owners can find all possible types of non-trivial insights based on latest research. We provide a plug-n-play system as a set of Dataset, Transformer pipeline, and Analytics APIs for both kinds of users. We show a usefulness measure of generated insights across various types of analytics in the system. We believe that this platform can be used to bridge the gap between time-series analytics and datasets by significantly reducing the time to find the true potential of budding time-series research and improving on it faster.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2019\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2019')\"\n      onkeypress=\"toggleGroup('group_2019')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2019</span>\n      <span class=\"bibbase_group_count\">\n        \n        (11)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"muise-chakraborti-agarwal-bajgar-chaudhary-lastrasmontano-ondrej-vodolan-etal-planningforgoalorienteddialoguesystems-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://arxiv.org/pdf/1910.08137\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'muise-chakraborti-agarwal-bajgar-chaudhary-lastrasmontano-ondrej-vodolan-etal-planningforgoalorienteddialoguesystems-2019', 'https://arxiv.org/pdf/1910.08137', event);\">\n    Planning for Goal-Oriented Dialogue Systems.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Chakraborti, T.; Agarwal, S.; Bajgar, O.; Chaudhary, A.; Lastras-Montano, L. A.; Ondrej, J.; Vodolan, M.;  and Wiecha, C.\n</span>\n\n  \n\n</span>\n\n   2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://arxiv.org/pdf/1910.08137\"\n     onclick=\"log_download('muise-chakraborti-agarwal-bajgar-chaudhary-lastrasmontano-ondrej-vodolan-etal-planningforgoalorienteddialoguesystems-2019', 'https://arxiv.org/pdf/1910.08137', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Planning for Goal-Oriented Dialogue Systems [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-chakraborti-agarwal-bajgar-chaudhary-lastrasmontano-ondrej-vodolan-etal-planningforgoalorienteddialoguesystems-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>47 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-chakraborti-agarwal-bajgar-chaudhary-lastrasmontano-ondrej-vodolan-etal-planningforgoalorienteddialoguesystems-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@misc{muise2019planning,\n    title={Planning for Goal-Oriented Dialogue Systems},\n    author={Christian Muise and Tathagata Chakraborti and Shubham Agarwal and Ondrej Bajgar and Arunima Chaudhary and Luis A. Lastras-Montano and Josef Ondrej and Miroslav Vodolan and Charlie Wiecha},\n    year={2019},\n    eprint={1910.08137},\n    archivePrefix={arXiv},\n    primaryClass={cs.AI},\n    url={https://arxiv.org/pdf/1910.08137},\n    abstract={Generating complex multi-turn goal-oriented dialogue agents is a difficult problem that has seen a considerable focus from many leaders in the tech industry, including IBM, Google, Amazon, and Microsoft. This is in large part due to the rapidly growing market demand for dialogue agents capable of goal-oriented behaviour. Due to the business process nature of these conversations, end-to-end machine learning systems are generally not a viable option, as the generated dialogue agents must be deployable and verifiable on behalf of the businesses authoring them. In this work, we propose a paradigm shift in the creation of goal-oriented complex dialogue systems that dramatically eliminates the need for a designer to manually specify a dialogue tree, which nearly all current systems have to resort to when the interaction pattern falls outside standard patterns such as slot filling. We propose a declarative representation of the dialogue agent to be processed by state-of-the-art planning technology. Our proposed approach covers all aspects of the process; from model solicitation to the execution of the generated plans/dialogue agents. Along the way, we introduce novel planning encodings for declarative dialogue synthesis, a variety of interfaces for working with the specification as a dialogue architect, and a robust executor for generalized contingent plans. We have created prototype implementations of all components, and in this paper, we further demonstrate the resulting system empirically.}\n}\n\n\n\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Generating complex multi-turn goal-oriented dialogue agents is a difficult problem that has seen a considerable focus from many leaders in the tech industry, including IBM, Google, Amazon, and Microsoft. This is in large part due to the rapidly growing market demand for dialogue agents capable of goal-oriented behaviour. Due to the business process nature of these conversations, end-to-end machine learning systems are generally not a viable option, as the generated dialogue agents must be deployable and verifiable on behalf of the businesses authoring them. In this work, we propose a paradigm shift in the creation of goal-oriented complex dialogue systems that dramatically eliminates the need for a designer to manually specify a dialogue tree, which nearly all current systems have to resort to when the interaction pattern falls outside standard patterns such as slot filling. We propose a declarative representation of the dialogue agent to be processed by state-of-the-art planning technology. Our proposed approach covers all aspects of the process; from model solicitation to the execution of the generated plans/dialogue agents. Along the way, we introduce novel planning encodings for declarative dialogue synthesis, a variety of interfaces for working with the specification as a dialogue architect, and a robust executor for generalized contingent plans. We have created prototype implementations of all components, and in this paper, we further demonstrate the resulting system empirically.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"vukovic-gerard-katz-shwartz-sohrabi-muise-rofrano-kalia-etal-towardsautomatedaiplanningforenterpriseservicesopportunitiesandchallenges-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://link.springer.com/chapter/10.1007%2F978-3-030-33702-5_6\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'vukovic-gerard-katz-shwartz-sohrabi-muise-rofrano-kalia-etal-towardsautomatedaiplanningforenterpriseservicesopportunitiesandchallenges-2019', 'https://link.springer.com/chapter/10.1007%2F978-3-030-33702-5_6', event);\">\n    Towards Automated AI Planning for Enterprise Services: Opportunities and Challenges.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Vukovic, M.; Gerard, S.; Katz, M.; Shwartz, L.; Sohrabi, S.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Rofrano, J.; Kalia, A.; Hwang, J.; Dang, Y. B.; Ma, J. D.;  and Jiang, Z. X.\n</span>\n\n  \n\n</span>\n\n  In <i>The 17th International Conference on Service-Oriented Computing (ICSOC-2019)</i>,  2019. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://link.springer.com/chapter/10.1007%2F978-3-030-33702-5_6\"\n     onclick=\"log_download('vukovic-gerard-katz-shwartz-sohrabi-muise-rofrano-kalia-etal-towardsautomatedaiplanningforenterpriseservicesopportunitiesandchallenges-2019', 'https://link.springer.com/chapter/10.1007%2F978-3-030-33702-5_6', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Towards Automated AI Planning for Enterprise Services: Opportunities and Challenges [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/vukovic-gerard-katz-shwartz-sohrabi-muise-rofrano-kalia-etal-towardsautomatedaiplanningforenterpriseservicesopportunitiesandchallenges-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>3 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('vukovic-gerard-katz-shwartz-sohrabi-muise-rofrano-kalia-etal-towardsautomatedaiplanningforenterpriseservicesopportunitiesandchallenges-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{vukovic-icsoc19,\n  title={Towards Automated AI Planning for Enterprise Services: Opportunities and Challenges},\n  author={Maja Vukovic and Scott Gerard and Michael Katz and Laura Shwartz and Shirin Sohrabi and Christian Muise and John Rofrano and Anup Kalia and Jinho Hwang and Ya Bin Dang and Jie DJ Ma and Zhuo Xuan Jiang},\n  booktitle={The 17th International Conference on Service-Oriented Computing (ICSOC-2019)},\n  year={2019},\n  url={https://link.springer.com/chapter/10.1007%2F978-3-030-33702-5_6},\n  abstract={Existing Artificial Intelligence (AI) driven automation solutions in enterprises employ machine learning, natural language processing, and chatbots. There is an opportunity for AI Planning to be applied, which offers reasoning about action trajectories to help build automation blueprints. AI Planning is a problem-solving technique, where knowledge about available actions and their consequences is used to identify a sequence of actions, which, when applied in a given initial state, satisfy a desired goal. AI Planning has successfully been applied in a number of domains ranging from space applications, logistics and transportation, manufacturing, robotics, scheduling, e-learning, enterprise risk management, and service composition. In this paper, we discuss experience in building automation solutions that employ AI planning for use in enterprise IT and business services, such as change and event management, migration and transformation and RPA composition. We discuss challenges in adoption of AI planning across the enterprise from implementation and deployment perspectives.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Existing Artificial Intelligence (AI) driven automation solutions in enterprises employ machine learning, natural language processing, and chatbots. There is an opportunity for AI Planning to be applied, which offers reasoning about action trajectories to help build automation blueprints. AI Planning is a problem-solving technique, where knowledge about available actions and their consequences is used to identify a sequence of actions, which, when applied in a given initial state, satisfy a desired goal. AI Planning has successfully been applied in a number of domains ranging from space applications, logistics and transportation, manufacturing, robotics, scheduling, e-learning, enterprise risk management, and service composition. In this paper, we discuss experience in building automation solutions that employ AI planning for use in enterprise IT and business services, such as change and event management, migration and transformation and RPA composition. We discuss challenges in adoption of AI planning across the enterprise from implementation and deployment perspectives.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"kim-muise-shah-agarwal-shah-bayesianinferenceoftemporalspecificationstoexplainhowplansdiffer-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.ijcai.org/proceedings/2019/0776.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'kim-muise-shah-agarwal-shah-bayesianinferenceoftemporalspecificationstoexplainhowplansdiffer-2019', 'https://www.ijcai.org/proceedings/2019/0776.pdf', event);\">\n    Bayesian Inference of Temporal Specifications to Explain How Plans Differ.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Kim, J.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Shah, A.; Agarwal, S.;  and Shah, J.\n</span>\n\n  \n\n</span>\n\n  In <i>The 28th International Joint Conference on Artificial Intelligence (IJCAI)</i>,  2019. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.ijcai.org/proceedings/2019/0776.pdf\"\n     onclick=\"log_download('kim-muise-shah-agarwal-shah-bayesianinferenceoftemporalspecificationstoexplainhowplansdiffer-2019', 'https://www.ijcai.org/proceedings/2019/0776.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Bayesian Inference of Temporal Specifications to Explain How Plans Differ [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/kim-muise-shah-agarwal-shah-bayesianinferenceoftemporalspecificationstoexplainhowplansdiffer-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>21 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('kim-muise-shah-agarwal-shah-bayesianinferenceoftemporalspecificationstoexplainhowplansdiffer-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{kim-ijcai19,\n  title={Bayesian Inference of Temporal Specifications to Explain How Plans Differ},\n  author={Joseph Kim and Christian Muise and Ankit Shah and Shubham Agarwal and Julie Shah},\n  booktitle={The 28th International Joint Conference on Artificial Intelligence (IJCAI)},\n  year={2019},\n  url={https://www.ijcai.org/proceedings/2019/0776.pdf},\n  abstract={Temporal logics are useful for describing dynamic system behavior, and have been successfully used as a language for goal definitions during task planning. Prior works on inferring temporal logic specifications have focused on &#x60;&#x60;summarizing&#x27;&#x27; the input dataset -- i.e., finding specifications that are satisfied by all plan traces belonging to the given set. In this paper, we examine the problem of inferring specifications that describe temporal differences between two sets of plan traces. We formalize the concept of providing such \\emph{contrastive} explanations, then present a Bayesian probabilistic model for inferring contrastive explanations as linear temporal logic specifications. We demonstrate the efficacy, scalability, and robustness of our model for inferring correct specifications across various benchmark planning domains and for a simulated air combat mission.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Temporal logics are useful for describing dynamic system behavior, and have been successfully used as a language for goal definitions during task planning. Prior works on inferring temporal logic specifications have focused on ``summarizing'' the input dataset – i.e., finding specifications that are satisfied by all plan traces belonging to the given set. In this paper, we examine the problem of inferring specifications that describe temporal differences between two sets of plan traces. We formalize the concept of providing such \\emphcontrastive explanations, then present a Bayesian probabilistic model for inferring contrastive explanations as linear temporal logic specifications. We demonstrate the efficacy, scalability, and robustness of our model for inferring correct specifications across various benchmark planning domains and for a simulated air combat mission.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"booth-muise-shah-evaluatingtheinterpretabilityoftheknowledgecompilationmapcommunicatinglogicalstatementseffectively-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.ijcai.org/proceedings/2019/0804.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'booth-muise-shah-evaluatingtheinterpretabilityoftheknowledgecompilationmapcommunicatinglogicalstatementseffectively-2019', 'https://www.ijcai.org/proceedings/2019/0804.pdf', event);\">\n    Evaluating the Interpretability of the Knowledge Compilation Map: Communicating Logical Statements Effectively.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Booth, S.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>;  and Shah, J.\n</span>\n\n  \n\n</span>\n\n  In <i>The 28th International Joint Conference on Artificial Intelligence (IJCAI)</i>,  2019. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.ijcai.org/proceedings/2019/0804.pdf\"\n     onclick=\"log_download('booth-muise-shah-evaluatingtheinterpretabilityoftheknowledgecompilationmapcommunicatinglogicalstatementseffectively-2019', 'https://www.ijcai.org/proceedings/2019/0804.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Evaluating the Interpretability of the Knowledge Compilation Map: Communicating Logical Statements Effectively [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/booth-muise-shah-evaluatingtheinterpretabilityoftheknowledgecompilationmapcommunicatinglogicalstatementseffectively-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>4 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('booth-muise-shah-evaluatingtheinterpretabilityoftheknowledgecompilationmapcommunicatinglogicalstatementseffectively-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{booth-ijcai19,\n  title={Evaluating the Interpretability of the Knowledge Compilation Map: Communicating Logical Statements Effectively},\n  author={Serena Booth and Christian Muise and Julie Shah},\n  booktitle={The 28th International Joint Conference on Artificial Intelligence (IJCAI)},\n  year={2019},\n  url={https://www.ijcai.org/proceedings/2019/0804.pdf},\n  abstract={Knowledge compilation techniques translate propositional theories into equivalent forms to increase their computational tractability. But, how should we best present these propositional theories to a human? We analyze the standard taxonomy of propositional theories for relative \\emph{interpretability} across three model domains: highway driving, emergency triage, and the chopsticks game. We generate decision-making agents which produce logical explanations for their actions and apply knowledge compilation to these explanations. Then, we evaluate how quickly, accurately, and confidently users comprehend the generated explanations to make decisions. We find that domain, formula size, and negated logical connectives significantly affect comprehension while formula properties typically associated with interpretability are not strong predictors of human ability to comprehend the theory.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Knowledge compilation techniques translate propositional theories into equivalent forms to increase their computational tractability. But, how should we best present these propositional theories to a human? We analyze the standard taxonomy of propositional theories for relative \\emphinterpretability across three model domains: highway driving, emergency triage, and the chopsticks game. We generate decision-making agents which produce logical explanations for their actions and apply knowledge compilation to these explanations. Then, we evaluate how quickly, accurately, and confidently users comprehend the generated explanations to make decisions. We find that domain, formula size, and negated logical connectives significantly affect comprehension while formula properties typically associated with interpretability are not strong predictors of human ability to comprehend the theory.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"sreedharan-chakraborti-muise-kambhampati-ageneralframeworkforsynthesizingandexecutingselfexplainingplansforhumanaiinteraction-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://openreview.net/pdf?id=H1gyE6hm5V\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'sreedharan-chakraborti-muise-kambhampati-ageneralframeworkforsynthesizingandexecutingselfexplainingplansforhumanaiinteraction-2019', 'https://openreview.net/pdf?id=H1gyE6hm5V', event);\">\n    A General Framework for Synthesizing and Executing Self-Explaining Plans for Human-AI Interaction.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Sreedharan, S.; Chakraborti, T.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>;  and Kambhampati, S.\n</span>\n\n  \n\n</span>\n\n  In <i>The 2nd ICAPS Workshop on Explainable Planning (XAIP-2019)</i>,  2019. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://openreview.net/pdf?id=H1gyE6hm5V\"\n     onclick=\"log_download('sreedharan-chakraborti-muise-kambhampati-ageneralframeworkforsynthesizingandexecutingselfexplainingplansforhumanaiinteraction-2019', 'https://openreview.net/pdf?id=H1gyE6hm5V', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"A General Framework for Synthesizing and Executing Self-Explaining Plans for Human-AI Interaction [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/sreedharan-chakraborti-muise-kambhampati-ageneralframeworkforsynthesizingandexecutingselfexplainingplansforhumanaiinteraction-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('sreedharan-chakraborti-muise-kambhampati-ageneralframeworkforsynthesizingandexecutingselfexplainingplansforhumanaiinteraction-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{sreedharan-xaip19,\n  title={A General Framework for Synthesizing and Executing Self-Explaining Plans for Human-AI Interaction},\n  author={Sarath Sreedharan and Tathagata Chakraborti and Christian Muise and Subbarao Kambhampati},\n  booktitle={The 2nd ICAPS Workshop on Explainable Planning (XAIP-2019)},\n  year={2019},\n  url={https://openreview.net/pdf?id=H1gyE6hm5V},\n  abstract={In this work, we present a general formulation for decision making in human-in-the-loop planning problems where the human’s expectations about an autonomous agent may differ from the agent’s own model. We show how our formulation for such multi-model planning problems allows us to capture existing approaches to this problem and also be used to generate novel explanatory behaviors. Our formulation also reveals a deep connection between multi-model planning and epistemic planning and we show how we can leverage classical planning compilations designed for epistemic planning for solving multi-model planning problems. We empirically show how this new compilation provides a computational advantage over previous approaches that separate reasoning about model reconciliation and identifying the agent’s plan.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  In this work, we present a general formulation for decision making in human-in-the-loop planning problems where the human’s expectations about an autonomous agent may differ from the agent’s own model. We show how our formulation for such multi-model planning problems allows us to capture existing approaches to this problem and also be used to generate novel explanatory behaviors. Our formulation also reveals a deep connection between multi-model planning and epistemic planning and we show how we can leverage classical planning compilations designed for epistemic planning for solving multi-model planning problems. We empirically show how this new compilation provides a computational advantage over previous approaches that separate reasoning about model reconciliation and identifying the agent’s plan.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"muise-vodolan-agarwal-bajgar-lastras-ondrej-executingcontingentplansaddressingchallengesindeployingartificialagents-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://icaps19.icaps-conference.org/workshops/IntEx/IntEx-2019-proceedings.pdf#page=32\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'muise-vodolan-agarwal-bajgar-lastras-ondrej-executingcontingentplansaddressingchallengesindeployingartificialagents-2019', 'https://icaps19.icaps-conference.org/workshops/IntEx/IntEx-2019-proceedings.pdf#page=32', event);\">\n    Executing Contingent Plans: Addressing Challenges in Deploying Artificial Agents.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Vodolan, M.; Agarwal, S.; Bajgar, O.; Lastras, L.;  and Ondrej, J.\n</span>\n\n  \n\n</span>\n\n  In <i>ICAPS Workshop on Integrating Planning, Acting, and Execution (IntEx)</i>,  2019. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://icaps19.icaps-conference.org/workshops/IntEx/IntEx-2019-proceedings.pdf#page=32\"\n     onclick=\"log_download('muise-vodolan-agarwal-bajgar-lastras-ondrej-executingcontingentplansaddressingchallengesindeployingartificialagents-2019', 'https://icaps19.icaps-conference.org/workshops/IntEx/IntEx-2019-proceedings.pdf#page=32', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Executing Contingent Plans: Addressing Challenges in Deploying Artificial Agents [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-vodolan-agarwal-bajgar-lastras-ondrej-executingcontingentplansaddressingchallengesindeployingartificialagents-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>3 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-vodolan-agarwal-bajgar-lastras-ondrej-executingcontingentplansaddressingchallengesindeployingartificialagents-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{muise-intex19,\n  title={Executing Contingent Plans: Addressing Challenges in Deploying Artificial Agents},\n  author={Christian Muise and Miroslav Vodolan and Shubham Agarwal and Ondrej Bajgar and Luis Lastras and Josef Ondrej},\n  booktitle={ICAPS Workshop on Integrating Planning, Acting, and Execution (IntEx)},\n  year={2019},\n  url = {https://icaps19.icaps-conference.org/workshops/IntEx/IntEx-2019-proceedings.pdf#page=32},\n  abstract={The vast majority of research in automated planning focuses on generating a plan from an initial problem specification; from the theoretical properties of this task to the implementation details required to do so efficiently. While such work is often motivated by practical applications, there is far less understanding of the issues associated with executing plans in online environments. In this work we focus on this understudied area, and the challenges / opportunities that arise when executing complex plans. Unlike many works in plan execution, we consider a form of contingent plans as the source for execution; their complexity stems from the sophisticated representation of the action effects used to model the uncertainty in the world. The key contribution of our work is a proposed executor that can reason using the sophisticated action effects, and we demonstrate the impact this can have empirically. In support of an effective executor, we also consider (1) the connection between the execution context and the planner&#x27;s view of the state of the world; and (2) the separation between the execution of an action (the aspect that affects the outside environment) and the realization of its effects (the aspect that captures what has actually changed).}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The vast majority of research in automated planning focuses on generating a plan from an initial problem specification; from the theoretical properties of this task to the implementation details required to do so efficiently. While such work is often motivated by practical applications, there is far less understanding of the issues associated with executing plans in online environments. In this work we focus on this understudied area, and the challenges / opportunities that arise when executing complex plans. Unlike many works in plan execution, we consider a form of contingent plans as the source for execution; their complexity stems from the sophisticated representation of the action effects used to model the uncertainty in the world. The key contribution of our work is a proposed executor that can reason using the sophisticated action effects, and we demonstrate the impact this can have empirically. In support of an effective executor, we also consider (1) the connection between the execution context and the planner's view of the state of the world; and (2) the separation between the execution of an action (the aspect that affects the outside environment) and the realization of its effects (the aspect that captures what has actually changed).\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"haslum-lipovetzky-magazzeni-muise-anintroductiontotheplanningdomaindefinitionlanguage-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.morganclaypoolpublishers.com/catalog_Orig/product_info.php?products_id=1384\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'haslum-lipovetzky-magazzeni-muise-anintroductiontotheplanningdomaindefinitionlanguage-2019', 'http://www.morganclaypoolpublishers.com/catalog_Orig/product_info.php?products_id=1384', event);\">\n    An Introduction to the Planning Domain Definition Language.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Haslum, P.; Lipovetzky, N.; Magazzeni, D.;  and <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  Morgan & Claypool,  2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.morganclaypoolpublishers.com/catalog_Orig/product_info.php?products_id=1384\"\n     onclick=\"log_download('haslum-lipovetzky-magazzeni-muise-anintroductiontotheplanningdomaindefinitionlanguage-2019', 'http://www.morganclaypoolpublishers.com/catalog_Orig/product_info.php?products_id=1384', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"An Introduction to the Planning Domain Definition Language [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/haslum-lipovetzky-magazzeni-muise-anintroductiontotheplanningdomaindefinitionlanguage-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>4 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('haslum-lipovetzky-magazzeni-muise-anintroductiontotheplanningdomaindefinitionlanguage-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@book{pddlbook,\n  author    = {Patrik Haslum and Nir Lipovetzky and Daniele Magazzeni and Christian Muise},\n  title     = {An Introduction to the Planning Domain Definition Language},\n  publisher = {Morgan \\&amp; Claypool},\n  year      = {2019},\n  isbn      = {9781627058759},\n  url       = {http://www.morganclaypoolpublishers.com/catalog_Orig/product_info.php?products_id=1384},\n  abstract  = {Planning is the branch of Artificial Intelligence (AI) that seeks to automate reasoning about plans, most importantly the reasoning that goes into formulating a plan to achieve a given goal in a given situation. AI planning is model-based: a planning system takes as input a description (or model) of the initial situation, the actions available to change it, and the goal condition to output a plan composed of those actions that will accomplish the goal when executed from the initial situation.\n\nThe Planning Domain Definition Language (PDDL) is a formal knowledge representation language designed to express planning models. Developed by the planning research community as a means of facilitating systems comparison, it has become a de-facto standard input\n\nlanguage of many planning systems, although it is not the only modelling language for planning. Several variants of PDDL have emerged that capture planning problems of different natures and complexities, with a focus on deterministic problems. The purpose of this book is two-fold. First, we present a unified and current account of PDDL, covering the subsets of PDDL that express discrete, numeric, temporal, and hybrid planning. Second, we want to introduce readers to the art of modelling planning problems in this language, through educational examples that demonstrate how PDDL is used to model realistic planning problems. The book is intended for advanced students and researchers in AI who want to dive into the mechanics of AI planning, as well as those who want to be able to use AI planning systems without an in-depth explanation of the algorithms and implementation techniques they use.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Planning is the branch of Artificial Intelligence (AI) that seeks to automate reasoning about plans, most importantly the reasoning that goes into formulating a plan to achieve a given goal in a given situation. AI planning is model-based: a planning system takes as input a description (or model) of the initial situation, the actions available to change it, and the goal condition to output a plan composed of those actions that will accomplish the goal when executed from the initial situation. The Planning Domain Definition Language (PDDL) is a formal knowledge representation language designed to express planning models. Developed by the planning research community as a means of facilitating systems comparison, it has become a de-facto standard input language of many planning systems, although it is not the only modelling language for planning. Several variants of PDDL have emerged that capture planning problems of different natures and complexities, with a focus on deterministic problems. The purpose of this book is two-fold. First, we present a unified and current account of PDDL, covering the subsets of PDDL that express discrete, numeric, temporal, and hybrid planning. Second, we want to introduce readers to the art of modelling planning problems in this language, through educational examples that demonstrate how PDDL is used to model realistic planning problems. The book is intended for advanced students and researchers in AI who want to dive into the mechanics of AI planning, as well as those who want to be able to use AI planning systems without an in-depth explanation of the algorithms and implementation techniques they use.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"dolejsi-long-fox-muise-fromaclassroomtoanindustryfrompddlhelloworldtodebuggingaplanningproblem-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.haz.ca/papers/icaps-demo-dolejsi-19.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'dolejsi-long-fox-muise-fromaclassroomtoanindustryfrompddlhelloworldtodebuggingaplanningproblem-2019', 'http://www.haz.ca/papers/icaps-demo-dolejsi-19.pdf', event);\">\n    From a Classroom to an Industry From PDDL “Hello World” to Debugging a Planning Problem.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Dolejsi, J.; Long, D.; Fox, M.;  and <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>System Demonstrations at the Twenty-Ninth International Conference on Automated Planning and Scheduling (ICAPS)</i>,  2019. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.haz.ca/papers/icaps-demo-dolejsi-19.pdf\"\n     onclick=\"log_download('dolejsi-long-fox-muise-fromaclassroomtoanindustryfrompddlhelloworldtodebuggingaplanningproblem-2019', 'http://www.haz.ca/papers/icaps-demo-dolejsi-19.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"From a Classroom to an Industry From PDDL “Hello World” to Debugging a Planning Problem [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/dolejsi-long-fox-muise-fromaclassroomtoanindustryfrompddlhelloworldtodebuggingaplanningproblem-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('dolejsi-long-fox-muise-fromaclassroomtoanindustryfrompddlhelloworldtodebuggingaplanningproblem-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{pdeditor-icaps19demo,\n  author    = {Jan Dolejsi and Derek Long and Maria Fox and Christian Muise},\n  title     = {From a Classroom to an Industry From PDDL “Hello World” to Debugging a Planning Problem},\n  booktitle = {System Demonstrations at the Twenty-Ninth International Conference on Automated Planning and Scheduling ({ICAPS})},\n  year      = {2019},\n  url       = {http://www.haz.ca/papers/icaps-demo-dolejsi-19.pdf},\n  abstract  = {This demonstration will build on previously shown end-to-end PDDL2.2 developer environ-ment built on top of Microsoft’s VS Code, but will expand substantially in two directions. First, the session from editor.planning.domains can seam-lessly migrate from the web browser to VS Code editor and take advantage of more elaborate PDDL support. Second, VS Code now integrates debugging tools for PDDL modelers such as search tree visualization, step-by-step search de-bugger and plan validation. This offers coherent set of tools to use from classroom to an industrial planning application debugging.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This demonstration will build on previously shown end-to-end PDDL2.2 developer environ-ment built on top of Microsoft’s VS Code, but will expand substantially in two directions. First, the session from editor.planning.domains can seam-lessly migrate from the web browser to VS Code editor and take advantage of more elaborate PDDL support. Second, VS Code now integrates debugging tools for PDDL modelers such as search tree visualization, step-by-step search de-bugger and plan validation. This offers coherent set of tools to use from classroom to an industrial planning application debugging.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"chakraborti-muise-agarwal-lastras-khazaeni-maianinterfacefordeclarativespecificationofgoaldirecteddialogueagents-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.haz.ca/papers/icaps-demo-chakraborti-19.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'chakraborti-muise-agarwal-lastras-khazaeni-maianinterfacefordeclarativespecificationofgoaldirecteddialogueagents-2019', 'http://www.haz.ca/papers/icaps-demo-chakraborti-19.pdf', event);\">\n    MAi: An Interface for Declarative Specification of Goal-Directed Dialogue Agents.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Chakraborti, T.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Agarwal, S.; Lastras, L. A.;  and Khazaeni, Y.\n</span>\n\n  \n\n</span>\n\n  In <i>System Demonstrations at the Twenty-Eighth International Conference on Automated Planning and Scheduling (ICAPS)</i>,  2019. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.haz.ca/papers/icaps-demo-chakraborti-19.pdf\"\n     onclick=\"log_download('chakraborti-muise-agarwal-lastras-khazaeni-maianinterfacefordeclarativespecificationofgoaldirecteddialogueagents-2019', 'http://www.haz.ca/papers/icaps-demo-chakraborti-19.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"MAi: An Interface for Declarative Specification of Goal-Directed Dialogue Agents [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/chakraborti-muise-agarwal-lastras-khazaeni-maianinterfacefordeclarativespecificationofgoaldirecteddialogueagents-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('chakraborti-muise-agarwal-lastras-khazaeni-maianinterfacefordeclarativespecificationofgoaldirecteddialogueagents-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{mai-icaps19demo,\n  author    = {Tathagata Chakraborti and Christian Muise and Shubham Agarwal and Luis A. Lastras and Yasaman Khazaeni},\n  title     = {{MAi}: An Interface for Declarative Specification of Goal-Directed Dialogue Agents},\n  booktitle = {System Demonstrations at the Twenty-Eighth International Conference on Automated Planning and Scheduling ({ICAPS})},\n  year      = {2019},\n  url       = {http://www.haz.ca/papers/icaps-demo-chakraborti-19.pdf},\n  abstract  = {The state of the art of dialogue agents requires a lengthy design process spanning months with experts in the loop who specify complex conversation patterns manually. Our work proposes a paradigm shift in bot design by adopting a declarative approach which composes the full dialog tree automatically. This allows the designer to construct complex dialogue agents from scratch and interact with them in a matter of hours. The demonstration will allow the audience to interact with this new design paradigm and construct their own bots on the spot.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The state of the art of dialogue agents requires a lengthy design process spanning months with experts in the loop who specify complex conversation patterns manually. Our work proposes a paradigm shift in bot design by adopting a declarative approach which composes the full dialog tree automatically. This allows the designer to construct complex dialogue agents from scratch and interact with them in a matter of hours. The demonstration will allow the audience to interact with this new design paradigm and construct their own bots on the spot.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"botea-muise-agarwal-alkan-bajgar-daly-kishimoto-lastras-etal-generatingdialogueagentsviaautomatedplanning-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.haz.ca/papers/deepdial-botea-19.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'botea-muise-agarwal-alkan-bajgar-daly-kishimoto-lastras-etal-generatingdialogueagentsviaautomatedplanning-2019', 'http://www.haz.ca/papers/deepdial-botea-19.pdf', event);\">\n    Generating Dialogue Agents via Automated Planning.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Botea, A.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Agarwal, S.; Alkan, O.; Bajgar, O.; Daly, E.; Kishimoto, A.; Lastras, L.; Marinescu, R.; Ondrej, J.; Pedemonte, P.;  and Vodolan, M.\n</span>\n\n  \n\n</span>\n\n  In <i>The Second AAAI Workshop On Reasoning And Learning For Human-Machine Dialogues (DEEP-DIAL 2019)</i>,  2019. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.haz.ca/papers/deepdial-botea-19.pdf\"\n     onclick=\"log_download('botea-muise-agarwal-alkan-bajgar-daly-kishimoto-lastras-etal-generatingdialogueagentsviaautomatedplanning-2019', 'http://www.haz.ca/papers/deepdial-botea-19.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Generating Dialogue Agents via Automated Planning [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/botea-muise-agarwal-alkan-bajgar-daly-kishimoto-lastras-etal-generatingdialogueagentsviaautomatedplanning-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('botea-muise-agarwal-alkan-bajgar-daly-kishimoto-lastras-etal-generatingdialogueagentsviaautomatedplanning-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{botea2019deepdial,\n  title={Generating Dialogue Agents via Automated Planning},\n  author={Adi Botea and Christian Muise and Shubham Agarwal and Oznur Alkan and Ondrej Bajgar and Elizabeth Daly and Akihiro Kishimoto and Luis Lastras and Radu Marinescu and Josef Ondrej and Pablo Pedemonte and Miroslav Vodolan},\n  booktitle={The Second AAAI Workshop On Reasoning And Learning For Human-Machine Dialogues (DEEP-DIAL 2019)},\n  year={2019},\n  url={http://www.haz.ca/papers/deepdial-botea-19.pdf},\n  abstract={Dialogue systems have many applications such as customer support or question answering. Typically they have been limited to shallow single turn interactions. However more advanced applications such as career coaching or planning a trip require a much more complex multi-turn dialogue. Current limitations of conversational systems have made it difficult to support applications that require personalization, customization and context dependent interactions. We tackle this challenging problem by using domain-independent AI planning to automatically create dialogue plans, customized to guide a dialogue towards achieving a given goal. The input includes a library of atomic dialogue actions, an initial state of the dialogue, and a goal. Dialogue plans are plugged into a dialogue system capable to orchestrate their execution. Use cases demonstrate the viability of the approach. Our work on dialogue planning has been integrated into a product, and it is in the process of being deployed into another.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Dialogue systems have many applications such as customer support or question answering. Typically they have been limited to shallow single turn interactions. However more advanced applications such as career coaching or planning a trip require a much more complex multi-turn dialogue. Current limitations of conversational systems have made it difficult to support applications that require personalization, customization and context dependent interactions. We tackle this challenging problem by using domain-independent AI planning to automatically create dialogue plans, customized to guide a dialogue towards achieving a given goal. The input includes a library of atomic dialogue actions, an initial state of the dialogue, and a goal. Dialogue plans are plugged into a dialogue system capable to orchestrate their execution. Use cases demonstrate the viability of the approach. Our work on dialogue planning has been integrated into a product, and it is in the process of being deployed into another.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"chakraborti-muise-agarwal-lastras-maianintelligentmodelacquisitioninterfaceforinteractivespecificationofdialogagents-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.haz.ca/papers/aaai-demo-chakraborti-19.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'chakraborti-muise-agarwal-lastras-maianintelligentmodelacquisitioninterfaceforinteractivespecificationofdialogagents-2019', 'http://www.haz.ca/papers/aaai-demo-chakraborti-19.pdf', event);\">\n    MAi: An Intelligent Model Acquisition Interface for Interactive Specification of Dialog Agents.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Chakraborti, T.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Agarwal, S.;  and Lastras, L. A.\n</span>\n\n  \n\n</span>\n\n  In <i>AAAI 2019 System Demonstration Track</i>,  2019. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.haz.ca/papers/aaai-demo-chakraborti-19.pdf\"\n     onclick=\"log_download('chakraborti-muise-agarwal-lastras-maianintelligentmodelacquisitioninterfaceforinteractivespecificationofdialogagents-2019', 'http://www.haz.ca/papers/aaai-demo-chakraborti-19.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"MAi: An Intelligent Model Acquisition Interface for Interactive Specification of Dialog Agents [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/chakraborti-muise-agarwal-lastras-maianintelligentmodelacquisitioninterfaceforinteractivespecificationofdialogagents-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('chakraborti-muise-agarwal-lastras-maianintelligentmodelacquisitioninterfaceforinteractivespecificationofdialogagents-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{mai-aaai19demo,\n  author    = {Tathagata Chakraborti and Christian Muise and Shubham Agarwal and Luis A. Lastras},\n  title     = {{MAi}: An Intelligent Model Acquisition Interface for Interactive Specification of Dialog Agents},\n  booktitle = {AAAI 2019 System Demonstration Track},\n  year      = {2019},\n  url       = {http://www.haz.ca/papers/aaai-demo-chakraborti-19.pdf},\n  abstract  = {The state of the art in automated conversational agents for enterprise (e.g. for customer support) require a lengthy design process with experts in the loop who have to figure out and specify complex conversation patterns. This demonstration looks at a prototype interface that aims to bring down the expertise required to design such agents as well as the time taken to do so. Specifically, we will focus on how a metawriter can assist the domain-writer during the design process and how complex conversation patterns can be derived from simplifying abstractions at the interface level.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The state of the art in automated conversational agents for enterprise (e.g. for customer support) require a lengthy design process with experts in the loop who have to figure out and specify complex conversation patterns. This demonstration looks at a prototype interface that aims to bring down the expertise required to design such agents as well as the time taken to do so. Specifically, we will focus on how a metawriter can assist the domain-writer during the design process and how complex conversation patterns can be derived from simplifying abstractions at the interface level.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2018\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2018')\"\n      onkeypress=\"toggleGroup('group_2018')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2018</span>\n      <span class=\"bibbase_group_count\">\n        \n        (11)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"muise-vodolan-agarwal-bajgar-lastras-executingcontingentplanschallengesindeployingartificialagents-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.haz.ca/papers/sip-muise-18.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'muise-vodolan-agarwal-bajgar-lastras-executingcontingentplanschallengesindeployingartificialagents-2018', 'http://www.haz.ca/papers/sip-muise-18.pdf', event);\">\n    Executing Contingent Plans: Challenges in Deploying Artificial Agents.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Vodolan, M.; Agarwal, S.; Bajgar, O.;  and Lastras, L.\n</span>\n\n  \n\n</span>\n\n  In <i>Fall Symposium on Integrating Planning, Diagnosis, and Causal Reasoning</i>,  2018. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.haz.ca/papers/sip-muise-18.pdf\"\n     onclick=\"log_download('muise-vodolan-agarwal-bajgar-lastras-executingcontingentplanschallengesindeployingartificialagents-2018', 'http://www.haz.ca/papers/sip-muise-18.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Executing Contingent Plans: Challenges in Deploying Artificial Agents [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-vodolan-agarwal-bajgar-lastras-executingcontingentplanschallengesindeployingartificialagents-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-vodolan-agarwal-bajgar-lastras-executingcontingentplanschallengesindeployingartificialagents-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{muise2018sip,\n  title={Executing Contingent Plans: Challenges in Deploying Artificial Agents},\n  author={Christian Muise and Miroslav Vodolan and Shubham Agarwal and Ondrej Bajgar and Luis Lastras},\n  booktitle={Fall Symposium on Integrating Planning, Diagnosis, and Causal Reasoning},\n  year={2018},\n  url={http://www.haz.ca/papers/sip-muise-18.pdf},\n  abstract={The vast majority of research in automated planning focuses on generating a plan from an initial problem specification, from the theoretical properties of this task to the implementation details required to do so efficiently. While such work is often motivated by practical applications, there is far less understanding of the issues associated with executing plans in online environments. In this work, we focus on some of the key challenges of executing contingent plans in a real-world deployment, and present some possible solutions.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The vast majority of research in automated planning focuses on generating a plan from an initial problem specification, from the theoretical properties of this task to the implementation details required to do so efficiently. While such work is often motivated by practical applications, there is far less understanding of the issues associated with executing plans in online environments. In this work, we focus on some of the key challenges of executing contingent plans in a real-world deployment, and present some possible solutions.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"muise-characterizingandcomputingalldeleterelaxeddeadends-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.haz.ca/papers/coplas-muise-18.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'muise-characterizingandcomputingalldeleterelaxeddeadends-2018', 'http://www.haz.ca/papers/coplas-muise-18.pdf', event);\">\n    Characterizing and Computing All Delete-Relaxed Dead-ends.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>Workshop on Constraint Satisfaction Techniques for Planning and Scheduling Problems (COPLAS'18)</i>,  2018. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.haz.ca/papers/coplas-muise-18.pdf\"\n     onclick=\"log_download('muise-characterizingandcomputingalldeleterelaxeddeadends-2018', 'http://www.haz.ca/papers/coplas-muise-18.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Characterizing and Computing All Delete-Relaxed Dead-ends [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-characterizingandcomputingalldeleterelaxeddeadends-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-characterizingandcomputingalldeleterelaxeddeadends-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{muise2018drde,\n  title={Characterizing and Computing All Delete-Relaxed Dead-ends},\n  author={Christian Muise},\n  booktitle={Workshop on Constraint Satisfaction Techniques for Planning and Scheduling Problems (COPLAS&#x27;18)},\n  year={2018},\n  url={http://www.haz.ca/papers/coplas-muise-18.pdf},\n  abstract={Dead-end detection is a key challenge in automated planning,\nand it is rapidly growing in popularity. Effective dead-end\ndetection techniques can have a large impact on the strength\nof a planner, and so the effective computation of dead-ends is\ncentral to many planning approaches. One of the better understood\ntechniques for detecting dead-ends is to focus on the\ndelete relaxation of a planning problem, where dead-end detection\nis a polynomial-time operation. In this work, we provide\na logical characterization for not just a single dead-end,\nbut for every delete-relaxed dead-end in a planning problem.\nWith a logical representation in hand, one could compile the\nrepresentation into a form amenable to effective reasoning.\nWe lay the ground-work for this larger vision and provide a\npreliminary evaluation to this end.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Dead-end detection is a key challenge in automated planning, and it is rapidly growing in popularity. Effective dead-end detection techniques can have a large impact on the strength of a planner, and so the effective computation of dead-ends is central to many planning approaches. One of the better understood techniques for detecting dead-ends is to focus on the delete relaxation of a planning problem, where dead-end detection is a polynomial-time operation. In this work, we provide a logical characterization for not just a single dead-end, but for every delete-relaxed dead-end in a planning problem. With a logical representation in hand, one could compile the representation into a form amenable to effective reasoning. We lay the ground-work for this larger vision and provide a preliminary evaluation to this end.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"koenig-muise-sanner-nontraditionalobjectivefunctionsformdps-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.haz.ca/papers/grw-koenig-18.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'koenig-muise-sanner-nontraditionalobjectivefunctionsformdps-2018', 'http://www.haz.ca/papers/grw-koenig-18.pdf', event);\">\n    Non-Traditional Objective Functions for MDPs.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Koenig, S.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>;  and Sanner, S.\n</span>\n\n  \n\n</span>\n\n  In <i>6th Goal Reasoning Workshop</i>,  2018. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.haz.ca/papers/grw-koenig-18.pdf\"\n     onclick=\"log_download('koenig-muise-sanner-nontraditionalobjectivefunctionsformdps-2018', 'http://www.haz.ca/papers/grw-koenig-18.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Non-Traditional Objective Functions for MDPs [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/koenig-muise-sanner-nontraditionalobjectivefunctionsformdps-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('koenig-muise-sanner-nontraditionalobjectivefunctionsformdps-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{koenig2018grw,\n  title={Non-Traditional Objective Functions for MDPs},\n  author={Sven Koenig and Christian Muise and Scott Sanner},\n  booktitle={6th Goal Reasoning Workshop},\n  year={2018},\n  url={http://www.haz.ca/papers/grw-koenig-18.pdf},\n  abstract={While probabilistic planning in AI research has\nlargely focused on cost-optimal goal-based objectives,\nwe argue that many realistic planning problems\nrequire more complex objective functions and\ndifferent perspectives on goals than is commonly\nfound in the literature. In this paper, we try to understand\nthe existing focus of probabilistic planning\non cost-optimal goal-based objectives, then\nwe proceed to outline some early and current AI research\non non-traditional objectives. We conclude\nby charging AI researchers to focus more on realistic\nobjectives to make probabilistic planning more\nattractive for actual applications.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  While probabilistic planning in AI research has largely focused on cost-optimal goal-based objectives, we argue that many realistic planning problems require more complex objective functions and different perspectives on goals than is commonly found in the literature. In this paper, we try to understand the existing focus of probabilistic planning on cost-optimal goal-based objectives, then we proceed to outline some early and current AI research on non-traditional objectives. We conclude by charging AI researchers to focus more on realistic objectives to make probabilistic planning more attractive for actual applications.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"bhargava-vaquero-williams-muise-managingcommunicationcostsundertemporaluncertainty-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.ijcai.org/proceedings/2018/0648.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'bhargava-vaquero-williams-muise-managingcommunicationcostsundertemporaluncertainty-2018', 'https://www.ijcai.org/proceedings/2018/0648.pdf', event);\">\n    Managing Communication Costs under Temporal Uncertainty.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Bhargava, N.; Vaquero, T.; Williams, B.;  and <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI)</i>,  2018. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.ijcai.org/proceedings/2018/0648.pdf\"\n     onclick=\"log_download('bhargava-vaquero-williams-muise-managingcommunicationcostsundertemporaluncertainty-2018', 'https://www.ijcai.org/proceedings/2018/0648.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Managing Communication Costs under Temporal Uncertainty [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/bhargava-vaquero-williams-muise-managingcommunicationcostsundertemporaluncertainty-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('bhargava-vaquero-williams-muise-managingcommunicationcostsundertemporaluncertainty-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{bhar-vaq-wil-mui-ijcai18,\n  author    = {Nikhil Bhargava and\n               Tiago Vaquero and\n               Brian Williams and\n               Christian Muise},\n  title     = {Managing Communication Costs under Temporal Uncertainty},\n  booktitle = {Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ({IJCAI})},\n  year      = {2018},\n  url       = {https://www.ijcai.org/proceedings/2018/0648.pdf},\n  abstract  = {In multi-agent temporal planning, individual agents cannot know a priori when other agents will execute their actions and so treat those actions as uncertain. Only when others communicate the results of their actions can that uncertainty be resolved. If a full communication protocol is specified ahead of time, then delay controllability can be used to assess the feasibility of the temporal plan. However, agents often have flexibility in choosing when to communicate the results of their action. In this paper, we address the question of how to choose communication protocols that guarantee the feasibility of the original temporal plan subject to some cost associated with that communication. To do so, we introduce a means of extracting delay controllability conflicts and show how we can use these conflicts to more efficiently guide our search. We then present three conflict-directed search algorithms and explore the theoretical and empirical tradeoffs between the different approaches.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  In multi-agent temporal planning, individual agents cannot know a priori when other agents will execute their actions and so treat those actions as uncertain. Only when others communicate the results of their actions can that uncertainty be resolved. If a full communication protocol is specified ahead of time, then delay controllability can be used to assess the feasibility of the temporal plan. However, agents often have flexibility in choosing when to communicate the results of their action. In this paper, we address the question of how to choose communication protocols that guarantee the feasibility of the original temporal plan subject to some cost associated with that communication. To do so, we introduce a means of extracting delay controllability conflicts and show how we can use these conflicts to more efficiently guide our search. We then present three conflict-directed search algorithms and explore the theoretical and empirical tradeoffs between the different approaches.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"bhargava-williams-muise-variabledelaycontrollability-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Variable-Delay Controllability.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Bhargava, N.; Williams, B.;  and <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI)</i>,  2018. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/bhargava-williams-muise-variabledelaycontrollability-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('bhargava-williams-muise-variabledelaycontrollability-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{bhar-wil-mui-ijcai18,\n  author    = {Nikhil Bhargava and\n               Brian Williams and\n               Christian Muise},\n  title     = {Variable-Delay Controllability},\n  booktitle = {Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ({IJCAI})},\n  year      = {2018},\n  abstract  = {In temporal planning, agents must schedule a set of events satisfying a set of predetermined constraints. These scheduling problems become more difficult when the duration of certain actions are outside the agent&#x27;s control. Delay controllability is the generalized notion of whether a schedule can be constructed in the face of uncertainty if the agent eventually learns when events occur. Our work introduces the substantially more complex setting of determining variable-delay controllability, where an agent learns about events after some unknown but bounded amount of time has passed. We provide an efficient O(n^3) variable-delay controllability checker and show how to create an execution strategy for variable-delay controllability problems. We also provide preliminary empirical evaluations of the quality of variable-delay controllability results as compared to approximations that use fixed delays to model the same problems. To our knowledge, these essential capabilities are absent from existing controllability checking algorithms.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  In temporal planning, agents must schedule a set of events satisfying a set of predetermined constraints. These scheduling problems become more difficult when the duration of certain actions are outside the agent's control. Delay controllability is the generalized notion of whether a schedule can be constructed in the face of uncertainty if the agent eventually learns when events occur. Our work introduces the substantially more complex setting of determining variable-delay controllability, where an agent learns about events after some unknown but bounded amount of time has passed. We provide an efficient O(n^3) variable-delay controllability checker and show how to create an execution strategy for variable-delay controllability problems. We also provide preliminary empirical evaluations of the quality of variable-delay controllability results as compared to approximations that use fixed delays to model the same problems. To our knowledge, these essential capabilities are absent from existing controllability checking algorithms.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"camacho-muise-baier-mcilraith-ltlrealizabilityviasafetyandreachabilitygames-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.ijcai.org/proceedings/2018/0651.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'camacho-muise-baier-mcilraith-ltlrealizabilityviasafetyandreachabilitygames-2018', 'https://www.ijcai.org/proceedings/2018/0651.pdf', event);\">\n    LTL Realizability via Safety and Reachability Games.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"http://www.albertocamacho.com/\">Camacho, A.</a>; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Baier, J. A.;  and <a class=\"bibbase author link\" href=\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\">McIlraith, S. A.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI)</i>,  2018. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.ijcai.org/proceedings/2018/0651.pdf\"\n     onclick=\"log_download('camacho-muise-baier-mcilraith-ltlrealizabilityviasafetyandreachabilitygames-2018', 'https://www.ijcai.org/proceedings/2018/0651.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"LTL Realizability via Safety and Reachability Games [pdf]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n  <a href=\"https://www.cs.toronto.edu/~acamacho/papers/cam-mui-bai-mci-ijcai18-poster.pdf\"\n     onclick=\"log_download('camacho-muise-baier-mcilraith-ltlrealizabilityviasafetyandreachabilitygames-2018', 'https://www.cs.toronto.edu/~acamacho/papers/cam-mui-bai-mci-ijcai18-poster.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"LTL Realizability via Safety and Reachability Games [pdf]\"\n       title=\" poster\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> poster</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/camacho-muise-baier-mcilraith-ltlrealizabilityviasafetyandreachabilitygames-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>5 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('camacho-muise-baier-mcilraith-ltlrealizabilityviasafetyandreachabilitygames-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{cam-mui-bai-mci-ijcai18,\n  author    = {Alberto Camacho and\n               Christian Muise and\n               Jorge A. Baier and\n               Sheila A. McIlraith},\n  title     = {{LTL} Realizability via Safety and Reachability Games},\n  booktitle = {Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ({IJCAI})},\n  year      = {2018},\n  url_paper = {https://www.ijcai.org/proceedings/2018/0651.pdf},\n  url_poster = {https://www.cs.toronto.edu/~acamacho/papers/cam-mui-bai-mci-ijcai18-poster.pdf},\n  abstract  = {LTL synthesis is the problem of computing a strategy that satisfies a given property expressed in Linear Temporal Logic (LTL). Synthesis provides a means of constructing programs that allow an agent to interact with their environment following a declarative specification of behavior. Modern approaches to LTL synthesis exploit bounded synthesis techniques, reducing the synthesis problem to a series of safety games between the agent and the environment. The same techniques can be used to determine unrealizability of LTL specifications by solving dual games where the order of turn taking is inverted. In the first part of the paper, we investigate the role of this duality in LTL realizability and synthesis. We describe different reductions to automata games that exploit duality to determine realizability, and introduce novel techniques that reduce realizability to a series of reachability games. In the second part of the paper, we introduce algorithms to solve these safety and reachability games via Fully Observable Non-Deterministic (FOND) planning. Our experimental evaluation illustrates that, by reducing the problem to a reachability game we can solve some problems that state-of-the-art synthesis tools cannot solve. Moreover, it shows that planning can be a competitive approach to LTL synthesis and realizability.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  LTL synthesis is the problem of computing a strategy that satisfies a given property expressed in Linear Temporal Logic (LTL). Synthesis provides a means of constructing programs that allow an agent to interact with their environment following a declarative specification of behavior. Modern approaches to LTL synthesis exploit bounded synthesis techniques, reducing the synthesis problem to a series of safety games between the agent and the environment. The same techniques can be used to determine unrealizability of LTL specifications by solving dual games where the order of turn taking is inverted. In the first part of the paper, we investigate the role of this duality in LTL realizability and synthesis. We describe different reductions to automata games that exploit duality to determine realizability, and introduce novel techniques that reduce realizability to a series of reachability games. In the second part of the paper, we introduce algorithms to solve these safety and reachability games via Fully Observable Non-Deterministic (FOND) planning. Our experimental evaluation illustrates that, by reducing the problem to a reachability game we can solve some problems that state-of-the-art synthesis tools cannot solve. Moreover, it shows that planning can be a competitive approach to LTL synthesis and realizability.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"camacho-muise-baier-mcilraith-synkitltlsynthesisasaservice-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.ijcai.org/proceedings/2018/0848.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'camacho-muise-baier-mcilraith-synkitltlsynthesisasaservice-2018', 'https://www.ijcai.org/proceedings/2018/0848.pdf', event);\">\n    SynKit: LTL Synthesis as a Service.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"http://www.albertocamacho.com/\">Camacho, A.</a>; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Baier, J. A.;  and <a class=\"bibbase author link\" href=\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\">McIlraith, S. A.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>Demonstration Track at the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI)</i>,  2018. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.ijcai.org/proceedings/2018/0848.pdf\"\n     onclick=\"log_download('camacho-muise-baier-mcilraith-synkitltlsynthesisasaservice-2018', 'https://www.ijcai.org/proceedings/2018/0848.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"SynKit: LTL Synthesis as a Service [pdf]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n  <a href=\"https://www.cs.toronto.edu/~acamacho/synkit\"\n     onclick=\"log_download('camacho-muise-baier-mcilraith-synkitltlsynthesisasaservice-2018', 'https://www.cs.toronto.edu/~acamacho/synkit', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"SynKit: LTL Synthesis as a Service [link]\"\n       title=\" link\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> link</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/camacho-muise-baier-mcilraith-synkitltlsynthesisasaservice-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>10 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('camacho-muise-baier-mcilraith-synkitltlsynthesisasaservice-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{cam-mui-bai-mci-ijcai18demo,\n  author    = {Alberto Camacho and\n               Christian Muise and\n               Jorge A. Baier and\n               Sheila A. McIlraith},\n  title     = {{SynKit}: {LTL} Synthesis as a Service},\n  booktitle = {Demonstration Track at the Twenty-Seventh International Joint Conference on Artificial Intelligence ({IJCAI})},\n  year      = {2018},\n  url_paper = {https://www.ijcai.org/proceedings/2018/0848.pdf},\n  url_link  = {https://www.cs.toronto.edu/~acamacho/synkit},\n  abstract  = {Automatic synthesis of software from specification is one of the classic problems in computer science. In the last decade, significant advances have been made in the synthesis of programs from specifications expressed in Linear Temporal Logic (LTL). LTL synthesis technology is central to a myriad of applications from the automated generation of controllers for Internet of Things devices, to the synthesis of control software for robotic applications. Unfortunately, the number of existing tools for LTL synthesis is limited, and using them requires specialized expertise. In this paper we present SynKit, a tool that offers LTL synthesis as a service. SynKit integrates a RESTful API and a web service with an editor, a solver, and a strategy visualizer.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Automatic synthesis of software from specification is one of the classic problems in computer science. In the last decade, significant advances have been made in the synthesis of programs from specifications expressed in Linear Temporal Logic (LTL). LTL synthesis technology is central to a myriad of applications from the automated generation of controllers for Internet of Things devices, to the synthesis of control software for robotic applications. Unfortunately, the number of existing tools for LTL synthesis is limited, and using them requires specialized expertise. In this paper we present SynKit, a tool that offers LTL synthesis as a service. SynKit integrates a RESTful API and a web service with an editor, a solver, and a strategy visualizer.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"camacho-muise-baier-mcilraith-synkitfiniteltlsynthesisasaservice-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://icaps18.icaps-conference.org/fileadmin/alg/conferences/icaps18/files/demos/2_SynKit_Demo_ICAPS_18.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'camacho-muise-baier-mcilraith-synkitfiniteltlsynthesisasaservice-2018', 'http://icaps18.icaps-conference.org/fileadmin/alg/conferences/icaps18/files/demos/2_SynKit_Demo_ICAPS_18.pdf', event);\">\n    SynKit: Finite LTL Synthesis as a Service.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"http://www.albertocamacho.com/\">Camacho, A.</a>; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Baier, J. A.;  and <a class=\"bibbase author link\" href=\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\">McIlraith, S. A.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>System Demonstrations at the Twenty-Eighth International Conference on Automated Planning and Scheduling (ICAPS)</i>,  2018. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"><span style=\"color: purple\">Best System Demonstration Award.</span></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://icaps18.icaps-conference.org/fileadmin/alg/conferences/icaps18/files/demos/2_SynKit_Demo_ICAPS_18.pdf\"\n     onclick=\"log_download('camacho-muise-baier-mcilraith-synkitfiniteltlsynthesisasaservice-2018', 'http://icaps18.icaps-conference.org/fileadmin/alg/conferences/icaps18/files/demos/2_SynKit_Demo_ICAPS_18.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"SynKit: Finite LTL Synthesis as a Service [pdf]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n  <a href=\"https://www.cs.toronto.edu/~acamacho/synkit\"\n     onclick=\"log_download('camacho-muise-baier-mcilraith-synkitfiniteltlsynthesisasaservice-2018', 'https://www.cs.toronto.edu/~acamacho/synkit', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"SynKit: Finite LTL Synthesis as a Service [link]\"\n       title=\" link\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> link</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/camacho-muise-baier-mcilraith-synkitfiniteltlsynthesisasaservice-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>14 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('camacho-muise-baier-mcilraith-synkitfiniteltlsynthesisasaservice-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{cam-mui-bai-mci-icaps18demo,\n  author    = {Alberto Camacho and\n               Christian Muise and\n               Jorge A. Baier and\n               Sheila A. McIlraith},\n  title     = {{SynKit}: Finite {LTL} Synthesis as a Service},\n  booktitle = {System Demonstrations at the Twenty-Eighth International Conference on Automated Planning and Scheduling ({ICAPS})},\n  year      = {2018},\n  url_paper = {http://icaps18.icaps-conference.org/fileadmin/alg/conferences/icaps18/files/demos/2_SynKit_Demo_ICAPS_18.pdf},\n  url_link = {https://www.cs.toronto.edu/~acamacho/synkit},\n  bibbase_note = {&lt;span style=&quot;color: purple&quot;&gt;Best System Demonstration Award.&lt;/span&gt;},\n  abstract = {Automatic synthesis of software from specification is one of the classical problems in computer science. Recent research has explored the use of finite linear temporal logic (LTLf) as a specification language. Engineers, researchers, and practitioners who wish to explore LTLf synthesis must overcome several barriers, including the lack of convenient tools to synthesize programs. In this paper we present SynKit, a web service that provides an LTLf synthesis capability. SynKit aims to simplify the task of synthesizing programs and debugging specifications. Offered as a web service, it is very accessible and does not require installation. SynKit integrates an editor, a solver, and a strategy visualizer.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Automatic synthesis of software from specification is one of the classical problems in computer science. Recent research has explored the use of finite linear temporal logic (LTLf) as a specification language. Engineers, researchers, and practitioners who wish to explore LTLf synthesis must overcome several barriers, including the lack of convenient tools to synthesize programs. In this paper we present SynKit, a web service that provides an LTLf synthesis capability. SynKit aims to simplify the task of synthesizing programs and debugging specifications. Offered as a web service, it is very accessible and does not require installation. SynKit integrates an editor, a solver, and a strategy visualizer.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"camacho-baier-muise-mcilraith-finiteltlsynthesisasplanning-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.cs.toronto.edu/~acamacho/papers/cam-bai-mui-mci-icaps18.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'camacho-baier-muise-mcilraith-finiteltlsynthesisasplanning-2018', 'http://www.cs.toronto.edu/~acamacho/papers/cam-bai-mui-mci-icaps18.pdf', event);\">\n    Finite LTL Synthesis as Planning.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"http://www.albertocamacho.com/\">Camacho, A.</a>; Baier, J. A.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>;  and <a class=\"bibbase author link\" href=\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\">McIlraith, S. A.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>Proceedings of the Twenty-Eight International Conference on Automated Planning and Scheduling (ICAPS)</i>,  2018. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.cs.toronto.edu/~acamacho/papers/cam-bai-mui-mci-icaps18.pdf\"\n     onclick=\"log_download('camacho-baier-muise-mcilraith-finiteltlsynthesisasplanning-2018', 'http://www.cs.toronto.edu/~acamacho/papers/cam-bai-mui-mci-icaps18.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Finite LTL Synthesis as Planning [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/camacho-baier-muise-mcilraith-finiteltlsynthesisasplanning-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>27 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('camacho-baier-muise-mcilraith-finiteltlsynthesisasplanning-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{cam-bai-mui-mci-icaps18,\n  author    = {Alberto Camacho and\n               Jorge A. Baier and\n               Christian Muise and\n               Sheila A. McIlraith},\n  title     = {Finite {LTL} Synthesis as Planning},\n  booktitle = {Proceedings of the Twenty-Eight International Conference on Automated Planning and Scheduling ({ICAPS})},\n  year      = {2018},\n  url       = {http://www.cs.toronto.edu/~acamacho/papers/cam-bai-mui-mci-icaps18.pdf},\n  abstract  = {LTL synthesis is the task of generating a strategy that satisfies a Linear Temporal Logic (LTL) specification interpreted over infinite traces. In this paper we examine the problem of LTLf synthesis, a variant of LTL synthesis where the specification of the behaviour of the strategy we generate is interpreted over finite traces – similar to the assumption we make in many planning problems, and important for the synthesis of business processes and other system interactions of finite duration. Existing approaches to LTLf synthesis transform LTLf into deterministic finite-state automata (DFA) and reduce the synthesis problem to a DFA game. Unfortunately, the DFA transformation is worst-case double-exponential in the size of the formula, presenting a computational bottleneck. In contrast, our approach exploits non-deterministic automata, and we reduce the synthesis problem to a non-deterministic planning problem. We leverage our approach not only for strategy generation but also to generate certificates of unrealizability – the first such method for LTLf. We employ a battery of techniques that exploit the structure of the LTLf specification to improve the efficiency of our transformation to automata. We combine these techniques with lazy determinization of automata and on-the-fly state abstraction. We illustrate the effectiveness of our approach on a set of established LTL synthesis benchmarks adapted to finite LTL.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  LTL synthesis is the task of generating a strategy that satisfies a Linear Temporal Logic (LTL) specification interpreted over infinite traces. In this paper we examine the problem of LTLf synthesis, a variant of LTL synthesis where the specification of the behaviour of the strategy we generate is interpreted over finite traces – similar to the assumption we make in many planning problems, and important for the synthesis of business processes and other system interactions of finite duration. Existing approaches to LTLf synthesis transform LTLf into deterministic finite-state automata (DFA) and reduce the synthesis problem to a DFA game. Unfortunately, the DFA transformation is worst-case double-exponential in the size of the formula, presenting a computational bottleneck. In contrast, our approach exploits non-deterministic automata, and we reduce the synthesis problem to a non-deterministic planning problem. We leverage our approach not only for strategy generation but also to generate certificates of unrealizability – the first such method for LTLf. We employ a battery of techniques that exploit the structure of the LTLf specification to improve the efficiency of our transformation to automata. We combine these techniques with lazy determinization of automata and on-the-fly state abstraction. We illustrate the effectiveness of our approach on a set of established LTL synthesis benchmarks adapted to finite LTL.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"camacho-baier-muise-mcilraith-synthesizingcontrollersonthecorrespondencebetweenltlsynthesisandnondeterministicplanning-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.cs.toronto.edu/~acamacho/papers/cam-bai-mui-mci-cai2018.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'camacho-baier-muise-mcilraith-synthesizingcontrollersonthecorrespondencebetweenltlsynthesisandnondeterministicplanning-2018', 'http://www.cs.toronto.edu/~acamacho/papers/cam-bai-mui-mci-cai2018.pdf', event);\">\n    Synthesizing Controllers: On the Correspondence Between LTL Synthesis and Non-deterministic Planning.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"http://www.albertocamacho.com/\">Camacho, A.</a>; Baier, J. A.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>;  and <a class=\"bibbase author link\" href=\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\">McIlraith, S. A.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>Advances in Artificial Intelligence - Proceedings of the Thirty-First Canadian Conference on Artificial Intelligence (CCAI)</i>, pages 45–59,  2018. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://doi.org/10.1007/978-3-319-89656-4_4\"\n     onclick=\"log_download('camacho-baier-muise-mcilraith-synthesizingcontrollersonthecorrespondencebetweenltlsynthesisandnondeterministicplanning-2018', 'https://doi.org/10.1007/978-3-319-89656-4_4', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Synthesizing Controllers: On the Correspondence Between LTL Synthesis and Non-deterministic Planning [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"http://www.cs.toronto.edu/~acamacho/papers/cam-bai-mui-mci-cai2018.pdf\"\n     onclick=\"log_download('camacho-baier-muise-mcilraith-synthesizingcontrollersonthecorrespondencebetweenltlsynthesisandnondeterministicplanning-2018', 'http://www.cs.toronto.edu/~acamacho/papers/cam-bai-mui-mci-cai2018.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Synthesizing Controllers: On the Correspondence Between LTL Synthesis and Non-deterministic Planning [pdf]\"\n       title=\" paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/camacho-baier-muise-mcilraith-synthesizingcontrollersonthecorrespondencebetweenltlsynthesisandnondeterministicplanning-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>5 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('camacho-baier-muise-mcilraith-synthesizingcontrollersonthecorrespondencebetweenltlsynthesisandnondeterministicplanning-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{cam-bai-mui-mci-ccai18,\n  author    = {Alberto Camacho and\n               Jorge A. Baier and\n               Christian Muise and\n               Sheila A. McIlraith},\n  title     = {Synthesizing Controllers: On the Correspondence Between {LTL} Synthesis and Non-deterministic Planning},\n  booktitle = {Advances in Artificial Intelligence - Proceedings of the Thirty-First Canadian Conference on Artificial Intelligence ({CCAI})},\n  pages     = {45--59},\n  year      = {2018},\n  url       = {https://doi.org/10.1007/978-3-319-89656-4_4},\n  url_paper = {http://www.cs.toronto.edu/~acamacho/papers/cam-bai-mui-mci-cai2018.pdf},\n  abstract  = {Linear Temporal Logic (LTL) synthesis can be understood as the problem of building a controller that defines a winning strategy, for a two-player game against the environment, where the objective is to satisfy a given LTL formula. It is an important problem with applications in software synthesis, including controller synthesis. In this paper we establish the correspondence between LTL synthesis and fully observable non-deterministic (FOND) planning. We study LTL interpreted over both finite and infinite traces. We also provide the first explicit compilation that translates an LTL synthesis problem to a FOND problem. Experiments with state-of-the-art LTL FOND and synthesis solvers show automated planning to be a viable and effective tool for highly structured LTL synthesis problems.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Linear Temporal Logic (LTL) synthesis can be understood as the problem of building a controller that defines a winning strategy, for a two-player game against the environment, where the objective is to satisfy a given LTL formula. It is an important problem with applications in software synthesis, including controller synthesis. In this paper we establish the correspondence between LTL synthesis and fully observable non-deterministic (FOND) planning. We study LTL interpreted over both finite and infinite traces. We also provide the first explicit compilation that translates an LTL synthesis problem to a FOND problem. Experiments with state-of-the-art LTL FOND and synthesis solvers show automated planning to be a viable and effective tool for highly structured LTL synthesis problems.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"chen-fang-muise-yu-shrobe-williams-radmaxriskanddeadlineawareplanningformaximumutility-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://groups.csail.mit.edu/mers/publication_uploads/Publications/2018/AAAI18Chen/radmaxAAAI18.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'chen-fang-muise-yu-shrobe-williams-radmaxriskanddeadlineawareplanningformaximumutility-2018', 'http://groups.csail.mit.edu/mers/publication_uploads/Publications/2018/AAAI18Chen/radmaxAAAI18.pdf', event);\">\n    RADMAX: Risk and Deadline Aware Planning for Maximum Utility.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Chen, J.; Fang, C.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Yu, P.; Shrobe, H. E.;  and Williams, B. C.\n</span>\n\n  \n\n</span>\n\n  In <i>AAAI Workshop on Artificial Intelligence for Cyber Security (AICS-2018)</i>, New Orleans, LA,  2018. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://groups.csail.mit.edu/mers/publication_uploads/Publications/2018/AAAI18Chen/radmaxAAAI18.pdf\"\n     onclick=\"log_download('chen-fang-muise-yu-shrobe-williams-radmaxriskanddeadlineawareplanningformaximumutility-2018', 'http://groups.csail.mit.edu/mers/publication_uploads/Publications/2018/AAAI18Chen/radmaxAAAI18.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"RADMAX: Risk and Deadline Aware Planning for Maximum Utility [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/chen-fang-muise-yu-shrobe-williams-radmaxriskanddeadlineawareplanningformaximumutility-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('chen-fang-muise-yu-shrobe-williams-radmaxriskanddeadlineawareplanningformaximumutility-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{Chen2018,\n address = {New Orleans, LA},\n author = {Jingkai Chen and Cheng Fang and Christian Muise and Peng Yu and Howard E. Shrobe and Brian C. Williams},\n booktitle = {AAAI Workshop on Artificial Intelligence for Cyber Security (AICS-2018)},\n title = {RADMAX: Risk and Deadline Aware Planning for Maximum Utility},\n year = {2018},\n url = {http://groups.csail.mit.edu/mers/publication_uploads/Publications/2018/AAAI18Chen/radmaxAAAI18.pdf},\n abstract = {Current  network  approaches  aim  to  maximize  network  utilization when routing flows. While such approaches are fast\nand usually result in acceptable behavior, existing methods are not\nmission aware. There is no concept of utility maximization, no capability to handle flows with specified deadlines and loss requirements, and no guarantees over the probability of network saturation.\nIn this paper, we present RADMAX: a system for Risk And\nDeadline Aware Planning for Maximum Utility based on constraint programming, which allows us to handle higher level\nmission  specifications.  We  show  the  correctness  of  RADMAX with respect to loss and delay bounds, provide results\nfor the optimality of RADMAX with respect to the mission\nutility,  and  review  current  results  on  computational  performance}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Current network approaches aim to maximize network utilization when routing flows. While such approaches are fast and usually result in acceptable behavior, existing methods are not mission aware. There is no concept of utility maximization, no capability to handle flows with specified deadlines and loss requirements, and no guarantees over the probability of network saturation. In this paper, we present RADMAX: a system for Risk And Deadline Aware Planning for Maximum Utility based on constraint programming, which allows us to handle higher level mission specifications. We show the correctness of RADMAX with respect to loss and delay bounds, provide results for the optimality of RADMAX with respect to the mission utility, and review current results on computational performance\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2017\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2017')\"\n      onkeypress=\"toggleGroup('group_2017')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2017</span>\n      <span class=\"bibbase_group_count\">\n        \n        (3)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"camacho-baier-muise-mcilraith-bridgingthegapbetweenltlsynthesisandautomatedplanning-2017\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.cs.toronto.edu/~sheila/publications/cam-etal-genplan17.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'camacho-baier-muise-mcilraith-bridgingthegapbetweenltlsynthesisandautomatedplanning-2017', 'http://www.cs.toronto.edu/~sheila/publications/cam-etal-genplan17.pdf', event);\">\n    Bridging the Gap Between LTL Synthesis and Automated Planning.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"http://www.albertocamacho.com/\">Camacho, A.</a>; Baier, J.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>;  and <a class=\"bibbase author link\" href=\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\">McIlraith, S. A.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>Workshop on Generalized Planning (GenPlan'17)</i>,  2017. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.cs.toronto.edu/~sheila/publications/cam-etal-genplan17.pdf\"\n     onclick=\"log_download('camacho-baier-muise-mcilraith-bridgingthegapbetweenltlsynthesisandautomatedplanning-2017', 'http://www.cs.toronto.edu/~sheila/publications/cam-etal-genplan17.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Bridging the Gap Between LTL Synthesis and Automated Planning [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/camacho-baier-muise-mcilraith-bridgingthegapbetweenltlsynthesisandautomatedplanning-2017\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>8 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('camacho-baier-muise-mcilraith-bridgingthegapbetweenltlsynthesisandautomatedplanning-2017')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{camacho-genplan-17,\n  title = {Bridging the Gap Between LTL Synthesis and Automated Planning},\n  author = {Alberto Camacho and Jorge Baier and Christian Muise and Sheila A. McIlraith},\n  booktitle = {Workshop on Generalized Planning ({GenPlan}&#x27;17)},\n  year = {2017},\n  url = {http://www.cs.toronto.edu/~sheila/publications/cam-etal-genplan17.pdf},\n  abstract={Linear Temporal Logic (LTL) synthesis can be understood as the problem of building a controller that defines a winning strategy, for a two-player game against the environment, where the objective is to satisfy a given LTL formula. It is an important problem with applications in software synthesis, including controller synthesis. Recent work has explored the close connection between automated planning and LTL synthesis but has not provided a full mapping between the two problems nor have its practical implications been explored. In this paper we establish the correspondence between LTL synthesis and fully observable non-deterministic (FOND) planning. We also provide the first explicit compilation that translates an LTL synthesis problem to a FOND problem. Experiments with state-of-the-art LTL FOND and synthesis solvers show automated planning to be a viable and effective tool for highly structured LTL synthesis problems.}\n}\n\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Linear Temporal Logic (LTL) synthesis can be understood as the problem of building a controller that defines a winning strategy, for a two-player game against the environment, where the objective is to satisfy a given LTL formula. It is an important problem with applications in software synthesis, including controller synthesis. Recent work has explored the close connection between automated planning and LTL synthesis but has not provided a full mapping between the two problems nor have its practical implications been explored. In this paper we establish the correspondence between LTL synthesis and fully observable non-deterministic (FOND) planning. We also provide the first explicit compilation that translates an LTL synthesis problem to a FOND problem. Experiments with state-of-the-art LTL FOND and synthesis solvers show automated planning to be a viable and effective tool for highly structured LTL synthesis problems.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"mombourquette-muise-mcilraith-logicalfilteringandsmoothingstateestimationinpartiallyobservabledomains-2017\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.haz.ca/papers/momb-aaai17.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'mombourquette-muise-mcilraith-logicalfilteringandsmoothingstateestimationinpartiallyobservabledomains-2017', 'http://www.haz.ca/papers/momb-aaai17.pdf', event);\">\n    Logical Filtering and Smoothing: State Estimation in Partially Observable Domains.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Mombourquette, B.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>;  and <a class=\"bibbase author link\" href=\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\">McIlraith, S.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>The 31st AAAI Conference on Artificial Intelligence</i>,  2017. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.haz.ca/papers/momb-aaai17.pdf\"\n     onclick=\"log_download('mombourquette-muise-mcilraith-logicalfilteringandsmoothingstateestimationinpartiallyobservabledomains-2017', 'http://www.haz.ca/papers/momb-aaai17.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Logical Filtering and Smoothing: State Estimation in Partially Observable Domains [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/mombourquette-muise-mcilraith-logicalfilteringandsmoothingstateestimationinpartiallyobservabledomains-2017\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('mombourquette-muise-mcilraith-logicalfilteringandsmoothingstateestimationinpartiallyobservabledomains-2017')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{bfr-aaai-17,\n  author = {Brent Mombourquette and Christian Muise and Sheila McIlraith},\n  year = {2017},\n  booktitle = {The 31st AAAI Conference on Artificial Intelligence},\n  keywords = {state estimation, belief tracking, logical filtering, approximation},\n  title = {Logical Filtering and Smoothing: State Estimation in Partially Observable Domains},\n  url = {http://www.haz.ca/papers/momb-aaai17.pdf},\n  abstract = {State estimation is the task of estimating the state of a partially\nobservable dynamical system given a sequence of executed\nactions and observations. In logical settings, state estimation\ncan be realized via logical filtering, which is exact\nbut can be intractable. We propose logical smoothing, a form\nof backwards reasoning that works in concert with approximated\nlogical filtering to refine past beliefs in light of new observations.\nWe characterize the notion of logical smoothing\ntogether with an algorithm for backwards-forwards state estimation.\nWe also present an approximation of our smoothing\nalgorithm that is space efficient. We prove properties of our\nalgorithms, and experimentally demonstrate their behaviour,\ncontrasting them with state estimation methods for planning.\nSmoothing and backwards-forwards reasoning are important\ntechniques for reasoning about partially observable dynamical\nsystems, introducing the logical analogue of effective\ntechniques from control theory and dynamic programming.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  State estimation is the task of estimating the state of a partially observable dynamical system given a sequence of executed actions and observations. In logical settings, state estimation can be realized via logical filtering, which is exact but can be intractable. We propose logical smoothing, a form of backwards reasoning that works in concert with approximated logical filtering to refine past beliefs in light of new observations. We characterize the notion of logical smoothing together with an algorithm for backwards-forwards state estimation. We also present an approximation of our smoothing algorithm that is space efficient. We prove properties of our algorithms, and experimentally demonstrate their behaviour, contrasting them with state estimation methods for planning. Smoothing and backwards-forwards reasoning are important techniques for reasoning about partially observable dynamical systems, introducing the logical analogue of effective techniques from control theory and dynamic programming.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"camacho-triantafillou-muise-baier-mcilraith-nondeterministicplanningwithtemporallyextendedgoalsltloverfiniteandinfinitetraces-2017\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.haz.ca/papers/camacho-aaai17.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'camacho-triantafillou-muise-baier-mcilraith-nondeterministicplanningwithtemporallyextendedgoalsltloverfiniteandinfinitetraces-2017', 'http://www.haz.ca/papers/camacho-aaai17.pdf', event);\">\n    Non-Deterministic Planning with Temporally Extended Goals: LTL over finite and infinite traces.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"http://www.albertocamacho.com/\">Camacho, A.</a>; Triantafillou, E.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Baier, J.;  and <a class=\"bibbase author link\" href=\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\">McIlraith, S. A.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>The 31st AAAI Conference on Artificial Intelligence</i>,  2017. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.haz.ca/papers/camacho-aaai17.pdf\"\n     onclick=\"log_download('camacho-triantafillou-muise-baier-mcilraith-nondeterministicplanningwithtemporallyextendedgoalsltloverfiniteandinfinitetraces-2017', 'http://www.haz.ca/papers/camacho-aaai17.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Non-Deterministic Planning with Temporally Extended Goals: LTL over finite and infinite traces [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/camacho-triantafillou-muise-baier-mcilraith-nondeterministicplanningwithtemporallyextendedgoalsltloverfiniteandinfinitetraces-2017\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>31 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('camacho-triantafillou-muise-baier-mcilraith-nondeterministicplanningwithtemporallyextendedgoalsltloverfiniteandinfinitetraces-2017')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{ltl-fond-aaai-17,\n  author = {Alberto Camacho and Eleni Triantafillou and Christian Muise and Jorge Baier and Sheila A. McIlraith},\n  year = {2017},\n  booktitle = {The 31st AAAI Conference on Artificial Intelligence},\n  keywords = {LTL, non-deterministic planning, temporally extended goals, FOND},\n  title = {Non-Deterministic Planning with Temporally Extended Goals: {LTL} over finite and infinite traces},\n  url = {http://www.haz.ca/papers/camacho-aaai17.pdf},\n  abstract = {Temporally extended goals are critical to the specification of a\ndiversity of real-world planning problems. Here we examine\nthe problem of non-deterministic planning with temporally\nextended goals specified in linear temporal logic (LTL), interpreted\nover either finite or infinite traces. Unlike existing\nLTL planners, we place no restrictions on our LTL formulae\nbeyond those necessary to distinguish finite from infinite\ninterpretations. We generate plans by compiling LTL temporally\nextended goals into problem instances described in\nthe Planning Domain Definition Language that are solved by\na state-of-the-art fully observable non-deterministic planner.\nWe propose several different compilations based on translations\nof LTL to alternating or non-deterministic (B¨uchi) automata,\nand evaluate various properties of the competing approaches.\nWe address a diverse spectrum of LTL planning\nproblems that, to this point, had not been solvable using AI\nplanning techniques, and do so in a manner that demonstrates\nhighly competitive performance.}\n}\n\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Temporally extended goals are critical to the specification of a diversity of real-world planning problems. Here we examine the problem of non-deterministic planning with temporally extended goals specified in linear temporal logic (LTL), interpreted over either finite or infinite traces. Unlike existing LTL planners, we place no restrictions on our LTL formulae beyond those necessary to distinguish finite from infinite interpretations. We generate plans by compiling LTL temporally extended goals into problem instances described in the Planning Domain Definition Language that are solved by a state-of-the-art fully observable non-deterministic planner. We propose several different compilations based on translations of LTL to alternating or non-deterministic (B¨uchi) automata, and evaluate various properties of the competing approaches. We address a diverse spectrum of LTL planning problems that, to this point, had not been solvable using AI planning techniques, and do so in a manner that demonstrates highly competitive performance.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2016\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2016')\"\n      onkeypress=\"toggleGroup('group_2016')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2016</span>\n      <span class=\"bibbase_group_count\">\n        \n        (14)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"muise-aflowshopscheduler-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://aosabook.org/en/500L/a-flow-shop-scheduler.html\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'muise-aflowshopscheduler-2016', 'http://aosabook.org/en/500L/a-flow-shop-scheduler.html', event);\">\n    A Flow Shop Scheduler.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  In Brown, A.;  and Dibernardo, M., editor(s), <i>500 Lines or Less</i>, 9. Lulu.com,  2016.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://aosabook.org/en/500L/a-flow-shop-scheduler.html\"\n     onclick=\"log_download('muise-aflowshopscheduler-2016', 'http://aosabook.org/en/500L/a-flow-shop-scheduler.html', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"A Flow Shop Scheduler [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-aflowshopscheduler-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-aflowshopscheduler-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{brown2016500,\n  author      = {Christian Muise},\n  title       = {A Flow Shop Scheduler},\n  editor      = {Brown, Amy and Dibernardo, Michael},\n  booktitle   = {500 Lines or Less},\n  publisher   = {Lulu.com},\n  year        = 2016,\n  %pages       = {},\n  url         = {http://aosabook.org/en/500L/a-flow-shop-scheduler.html},\n  chapter     = 9,\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"muise-dignum-felli-miller-pearce-sonenberg-towardsteamformationviaautomatedplanning-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://link.springer.com/chapter/10.1007%2F978-3-319-42691-4_16\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'muise-dignum-felli-miller-pearce-sonenberg-towardsteamformationviaautomatedplanning-2016', 'http://link.springer.com/chapter/10.1007%2F978-3-319-42691-4_16', event);\">\n    Towards Team Formation via Automated Planning.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Dignum, F.; Felli, P.; Miller, T.; Pearce, A. R.;  and Sonenberg, L.\n</span>\n\n  \n\n</span>\n\n  <i>Lecture Notes in Computer Science</i>, 9628: 282-299.  2016.\n  <span class=\"note\">Special Issue on Coordination, Organizations, Institutions, and Norms in Agent Systems XI</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://link.springer.com/chapter/10.1007%2F978-3-319-42691-4_16\"\n     onclick=\"log_download('muise-dignum-felli-miller-pearce-sonenberg-towardsteamformationviaautomatedplanning-2016', 'http://link.springer.com/chapter/10.1007%2F978-3-319-42691-4_16', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Towards Team Formation via Automated Planning [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-dignum-felli-miller-pearce-sonenberg-towardsteamformationviaautomatedplanning-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>3 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-dignum-felli-miller-pearce-sonenberg-towardsteamformationviaautomatedplanning-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{coin-journal-teamwork,\n  title = {Towards Team Formation via Automated Planning},\n  author = {Christian Muise and Frank Dignum and Paolo Felli and Tim Miller and Adrian R. Pearce and Liz Sonenberg},\n  journal = {Lecture Notes in Computer Science},\n  volume = {9628},\n  publisher = {Springer},\n  pages = {282-299},\n  year = {2016},\n  note = {Special Issue on Coordination, Organizations, Institutions, and Norms in Agent Systems XI},\n  keywords = {multi-agent planning, epistemic reasoning},\n  url = {http://link.springer.com/chapter/10.1007%2F978-3-319-42691-4_16},\n  abstract={Cooperative problem solving involves four key phases: (1) finding potential members to form a team, (2) forming the team, (3) formulating a plan for the team, and (4) executing the plan. We extend recent work on multi-agent epistemic planning and apply it to the problem of team formation in a blocksworld scenario. We provide an encoding of the first three phases of team formation from the perspective of an initiator, and show how automated planning efficiently yields conditional plans that guarantee certain collective intentions will be achieved. The expressiveness of the epistemic planning formalism, which supports modelling with the nested beliefs of agents, opens the prospect of broad applicability to the operationalisation of collective intention.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Cooperative problem solving involves four key phases: (1) finding potential members to form a team, (2) forming the team, (3) formulating a plan for the team, and (4) executing the plan. We extend recent work on multi-agent epistemic planning and apply it to the problem of team formation in a blocksworld scenario. We provide an encoding of the first three phases of team formation from the perspective of an initiator, and show how automated planning efficiently yields conditional plans that guarantee certain collective intentions will be achieved. The expressiveness of the epistemic planning formalism, which supports modelling with the nested beliefs of agents, opens the prospect of broad applicability to the operationalisation of collective intention.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"mombourquette-muise-mcilraith-beliefstateestimationforplanningviaapproximatelogicalfilteringandsmoothing-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://ceur-ws.org/Vol-1648/paper8.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'mombourquette-muise-mcilraith-beliefstateestimationforplanningviaapproximatelogicalfilteringandsmoothing-2016', 'http://ceur-ws.org/Vol-1648/paper8.pdf', event);\">\n    Belief State Estimation for Planning via Approximate Logical Filtering and Smoothing.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Mombourquette, B.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>;  and <a class=\"bibbase author link\" href=\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\">McIlraith, S. A.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>Workshop on Knowledge-based techniques for problem solving and reasoning (KnowProS'16)</i>,  2016. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://ceur-ws.org/Vol-1648/paper8.pdf\"\n     onclick=\"log_download('mombourquette-muise-mcilraith-beliefstateestimationforplanningviaapproximatelogicalfilteringandsmoothing-2016', 'http://ceur-ws.org/Vol-1648/paper8.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Belief State Estimation for Planning via Approximate Logical Filtering and Smoothing [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/mombourquette-muise-mcilraith-beliefstateestimationforplanningviaapproximatelogicalfilteringandsmoothing-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('mombourquette-muise-mcilraith-beliefstateestimationforplanningviaapproximatelogicalfilteringandsmoothing-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{mombourquette-knowpros16-bf,\n  title = {Belief State Estimation for Planning via Approximate Logical Filtering and Smoothing},\n  author = {Brent Mombourquette and Christian Muise and Sheila A. McIlraith},\n  booktitle = {Workshop on Knowledge-based techniques for problem solving and reasoning ({KnowProS}&#x27;16)},\n  year = {2016},\n  url = {http://ceur-ws.org/Vol-1648/paper8.pdf},\n  abstract={State estimation is the task of estimating the state of a partially observable dynamical system given a sequence of executed actions and observations. In logical settings, state estimation can be realized via logical filtering. Unfortunately such filtering, though exact, can be intractable. To this end, we propose logical smoothing, a form of backwards reasoning that works in concert with logical filtering to refine past beliefs in light of new observations. We characterize the notion of logical smoothing together with an algorithm for backwards-forwards state estimation. We prove properties of our algorithms, and experimentally demonstrate their behaviour. Smoothing together with backwards-forwards reasoning are important techniques for reasoning about partially observable dynamical systems, introducing the logical analogue of effective techniques from control theory and dynamic programming}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  State estimation is the task of estimating the state of a partially observable dynamical system given a sequence of executed actions and observations. In logical settings, state estimation can be realized via logical filtering. Unfortunately such filtering, though exact, can be intractable. To this end, we propose logical smoothing, a form of backwards reasoning that works in concert with logical filtering to refine past beliefs in light of new observations. We characterize the notion of logical smoothing together with an algorithm for backwards-forwards state estimation. We prove properties of our algorithms, and experimentally demonstrate their behaviour. Smoothing together with backwards-forwards reasoning are important techniques for reasoning about partially observable dynamical systems, introducing the logical analogue of effective techniques from control theory and dynamic programming\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"camacho-triantafillou-muise-baier-mcilraith-nondeterministicplanningwithtemporallyextendedgoalscompletingthestoryforfiniteandinfiniteltl-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://ceur-ws.org/Vol-1648/paper10.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'camacho-triantafillou-muise-baier-mcilraith-nondeterministicplanningwithtemporallyextendedgoalscompletingthestoryforfiniteandinfiniteltl-2016', 'http://ceur-ws.org/Vol-1648/paper10.pdf', event);\">\n    Non-Deterministic Planning with Temporally Extended Goals: Completing the story for finite and infinite LTL.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"http://www.albertocamacho.com/\">Camacho, A.</a>; Triantafillou, E.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Baier, J.;  and McIlraith, S. A.\n</span>\n\n  \n\n</span>\n\n  In <i>Workshop on Knowledge-based techniques for problem solving and reasoning (KnowProS'16)</i>,  2016. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://ceur-ws.org/Vol-1648/paper10.pdf\"\n     onclick=\"log_download('camacho-triantafillou-muise-baier-mcilraith-nondeterministicplanningwithtemporallyextendedgoalscompletingthestoryforfiniteandinfiniteltl-2016', 'http://ceur-ws.org/Vol-1648/paper10.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Non-Deterministic Planning with Temporally Extended Goals: Completing the story for finite and infinite LTL [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/camacho-triantafillou-muise-baier-mcilraith-nondeterministicplanningwithtemporallyextendedgoalscompletingthestoryforfiniteandinfiniteltl-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('camacho-triantafillou-muise-baier-mcilraith-nondeterministicplanningwithtemporallyextendedgoalscompletingthestoryforfiniteandinfiniteltl-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{camacho-knowpros16-ltl,\n  title = {Non-Deterministic Planning with Temporally Extended Goals: Completing the story for finite and infinite LTL},\n  author = {Alberto Camacho and Eleni Triantafillou and Christian Muise and Jorge Baier and Sheila A. McIlraith},\n  booktitle = {Workshop on Knowledge-based techniques for problem solving and reasoning ({KnowProS}&#x27;16)},\n  year = {2016},\n  url = {http://ceur-ws.org/Vol-1648/paper10.pdf},\n  abstract={Temporally extended goals are critical to the specification of a diversity of real-world planning problems. Here we examine the problem of planning with temporally extended goals over both finite and infinite traces where actions can be non-deterministic, and where temporally extended goals are specified in linear temporal logic (LTL). Unlike existing LTL planners, we place no restrictionson our LTL formulae beyond those necessary to distinguish finite from infinite trace interpretations. We realize our planner by compiling temporally extended goals, represented in LTL, into Planning Domain Definition Language problem instances, and exploiting a state-of-the-art fully observable non-deterministic planner to compute solutions. The resulting planner is sound and complete. Our approach exploits the correspondence between LTL and automata. We propose several different compilations based on translations of LTL to (Buchi) alternating or non-deterministic finite state automata, and evaluate various properties of the competing approaches. We address a diverse spectrum of LTL planning problems that, to this point, had not been solvable using AI planning techniques. We do so while demonstrating competitive performance relative to the state of the art in LTL planning.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Temporally extended goals are critical to the specification of a diversity of real-world planning problems. Here we examine the problem of planning with temporally extended goals over both finite and infinite traces where actions can be non-deterministic, and where temporally extended goals are specified in linear temporal logic (LTL). Unlike existing LTL planners, we place no restrictionson our LTL formulae beyond those necessary to distinguish finite from infinite trace interpretations. We realize our planner by compiling temporally extended goals, represented in LTL, into Planning Domain Definition Language problem instances, and exploiting a state-of-the-art fully observable non-deterministic planner to compute solutions. The resulting planner is sound and complete. Our approach exploits the correspondence between LTL and automata. We propose several different compilations based on translations of LTL to (Buchi) alternating or non-deterministic finite state automata, and evaluate various properties of the competing approaches. We address a diverse spectrum of LTL planning problems that, to this point, had not been solvable using AI planning techniques. We do so while demonstrating competitive performance relative to the state of the art in LTL planning.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"muise-beck-mcilraith-optimalpartialorderplanrelaxationviamaxsat-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.jair.org/media/5128/live-5128-9534-jair.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'muise-beck-mcilraith-optimalpartialorderplanrelaxationviamaxsat-2016', 'http://www.jair.org/media/5128/live-5128-9534-jair.pdf', event);\">\n    Optimal Partial-Order Plan Relaxation via MaxSAT.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Beck, J. C.;  and <a class=\"bibbase author link\" href=\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\">McIlraith, S. A.</a>\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Artificial Intelligence Research</i>.  2016.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.jair.org/media/5128/live-5128-9534-jair.pdf\"\n     onclick=\"log_download('muise-beck-mcilraith-optimalpartialorderplanrelaxationviamaxsat-2016', 'http://www.jair.org/media/5128/live-5128-9534-jair.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Optimal Partial-Order Plan Relaxation via MaxSAT [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-beck-mcilraith-optimalpartialorderplanrelaxationviamaxsat-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>3 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-beck-mcilraith-optimalpartialorderplanrelaxationviamaxsat-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{jair-popgen,\n  author    = {Christian Muise and J. Christopher Beck and Sheila A. McIlraith},\n  title     = {Optimal Partial-Order Plan Relaxation via MaxSAT},\n  journal   = {Journal of Artificial Intelligence Research},\n  year      = {2016},\n  url       = {http://www.jair.org/media/5128/live-5128-9534-jair.pdf},\n  abstract  = {Partial-order plans (POPs) are attractive because of their least-commitment nature, which provides enhanced plan flexibility at execution time relative to sequential plans. Current research on automated plan generation focuses on producing sequential plans, despite the appeal of POPs. In this paper we examine POP generation by relaxing or modifying the action orderings of a sequential plan to optimize for plan criteria that promote flexibility. Our approach relies on a novel partial weighted MaxSAT encoding of a sequential plan that supports the minimization of deordering or reordering of actions. We further demonstrate how to remove redundant actions from the plan. Our partial weighted MaxSAT encoding allows us to compute a POP from a sequential plan effectively. We compare the efficiency of our approach to previous methods for POP generation via sequential-plan relaxation. Our results show that while an existing heuristic approach consistently produces the optimal deordering of a sequential plan, our approach has greater flexibility when we consider reordering the actions in the plan while also providing a guarantee of optimality. We also investigate and confirm the accuracy of the standard $\\flex$ metric typically used to predict the true flexibility of a POP as measured by the number of linearizations it represents.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Partial-order plans (POPs) are attractive because of their least-commitment nature, which provides enhanced plan flexibility at execution time relative to sequential plans. Current research on automated plan generation focuses on producing sequential plans, despite the appeal of POPs. In this paper we examine POP generation by relaxing or modifying the action orderings of a sequential plan to optimize for plan criteria that promote flexibility. Our approach relies on a novel partial weighted MaxSAT encoding of a sequential plan that supports the minimization of deordering or reordering of actions. We further demonstrate how to remove redundant actions from the plan. Our partial weighted MaxSAT encoding allows us to compute a POP from a sequential plan effectively. We compare the efficiency of our approach to previous methods for POP generation via sequential-plan relaxation. Our results show that while an existing heuristic approach consistently produces the optimal deordering of a sequential plan, our approach has greater flexibility when we consider reordering the actions in the plan while also providing a guarantee of optimality. We also investigate and confirm the accuracy of the standard $\\flex$ metric typically used to predict the true flexibility of a POP as measured by the number of linearizations it represents.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"muise-planningdomains-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.haz.ca/papers/planning-domains-icaps16.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'muise-planningdomains-2016', 'http://www.haz.ca/papers/planning-domains-icaps16.pdf', event);\">\n    Planning.Domains.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>The 26th International Conference on Automated Planning and Scheduling - Demonstrations</i>,  2016. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.haz.ca/papers/planning-domains-icaps16.pdf\"\n     onclick=\"log_download('muise-planningdomains-2016', 'http://www.haz.ca/papers/planning-domains-icaps16.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Planning.Domains [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-planningdomains-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>7 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-planningdomains-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{muise-icaps16demo-pd,\n  title = {{Planning.Domains}},\n  author = {Christian Muise},\n  booktitle = {The 26th International Conference on Automated Planning and Scheduling - Demonstrations},\n  year = {2016},\n  url = {http://www.haz.ca/papers/planning-domains-icaps16.pdf},\n  abstract = {Commonly used resources for the field of automated planning, such as benchmarks, problem generators, etc., are widespread over the internet. With planning.domains, we aim to (a) collect these resources in a central location; and (b) enable creative possibilities through a consistent interface to the larger planning community. In this demo, we focus on the three main pillars of planning.domains: (1) api.planning.domains – a programmatic interface to all existing planning problems; (2) solver.planning.domains – an open (and extendable) interface to planning-in-the-cloud; and (3) editor.planning.domains - a fully featured editor for planning domains.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Commonly used resources for the field of automated planning, such as benchmarks, problem generators, etc., are widespread over the internet. With planning.domains, we aim to (a) collect these resources in a central location; and (b) enable creative possibilities through a consistent interface to the larger planning community. In this demo, we focus on the three main pillars of planning.domains: (1) api.planning.domains – a programmatic interface to all existing planning problems; (2) solver.planning.domains – an open (and extendable) interface to planning-in-the-cloud; and (3) editor.planning.domains - a fully featured editor for planning domains.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"camacho-triantafillou-muise-baier-mcilraith-ltlsynthesisfornondeterministicsystemsonfiniteandinfinitetraces-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.haz.ca/papers/camacho-hsdip16-ltl.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'camacho-triantafillou-muise-baier-mcilraith-ltlsynthesisfornondeterministicsystemsonfiniteandinfinitetraces-2016', 'http://www.haz.ca/papers/camacho-hsdip16-ltl.pdf', event);\">\n    LTL Synthesis for Non-Deterministic Systems on Finite and Infinite Traces.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Camacho, A.; Triantafillou, E.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Baier, J.;  and McIlraith, S. A.\n</span>\n\n  \n\n</span>\n\n  In <i>Workshop on Heuristic Search and Domain Independent Planning (HSDIP'16)</i>,  2016. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.haz.ca/papers/camacho-hsdip16-ltl.pdf\"\n     onclick=\"log_download('camacho-triantafillou-muise-baier-mcilraith-ltlsynthesisfornondeterministicsystemsonfiniteandinfinitetraces-2016', 'http://www.haz.ca/papers/camacho-hsdip16-ltl.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"LTL Synthesis for Non-Deterministic Systems on Finite and Infinite Traces [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/camacho-triantafillou-muise-baier-mcilraith-ltlsynthesisfornondeterministicsystemsonfiniteandinfinitetraces-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('camacho-triantafillou-muise-baier-mcilraith-ltlsynthesisfornondeterministicsystemsonfiniteandinfinitetraces-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{camacho-hsdip16-ltl,\n  title = {LTL Synthesis for Non-Deterministic Systems on Finite and Infinite Traces},\n  author = {Alberto Camacho and Eleni Triantafillou and Christian Muise and Jorge Baier and Sheila A. McIlraith},\n  booktitle = {Workshop on Heuristic Search and Domain Independent Planning ({HSDIP}&#x27;16)},\n  year = {2016},\n  url = {http://www.haz.ca/papers/camacho-hsdip16-ltl.pdf},\n  keywords = {non-deterministic planning, plan generation},\n  abstract={Temporally extended goals are critical to the specification of a diversity of real-world planning problems. Here we examine the problem of planning with temporally extended goals over both finite and infinite traces where actions can be non-deterministic, and where temporally extended goals are specified in linear temporal logic (LTL). Unlike existing LTL planners, we place no restrictions on our LTL formulae beyond those necessary to distinguish finite from infinite trace interpretations. We realize our planner by translating LTL goals into either (Büchi) alternating or non-deterministic finite state automata, and exploiting a state-of-the-art fully observable non-deterministic planner to compute solutions. The resulting planner is sound and complete. Our system addresses a diverse spectrum of LTL planning problems that to this point had not been solvable using AI planning techniques. We do so while demonstrating competitive performance relative to the state of the art in LTL planning.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Temporally extended goals are critical to the specification of a diversity of real-world planning problems. Here we examine the problem of planning with temporally extended goals over both finite and infinite traces where actions can be non-deterministic, and where temporally extended goals are specified in linear temporal logic (LTL). Unlike existing LTL planners, we place no restrictions on our LTL formulae beyond those necessary to distinguish finite from infinite trace interpretations. We realize our planner by translating LTL goals into either (Büchi) alternating or non-deterministic finite state automata, and exploiting a state-of-the-art fully observable non-deterministic planner to compute solutions. The resulting planner is sound and complete. Our system addresses a diverse spectrum of LTL planning problems that to this point had not been solvable using AI planning techniques. We do so while demonstrating competitive performance relative to the state of the art in LTL planning.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"miller-muise-beliefupdateforproperepistemicknowledgebases-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.haz.ca/papers/miller-ijcai-16.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'miller-muise-beliefupdateforproperepistemicknowledgebases-2016', 'http://www.haz.ca/papers/miller-ijcai-16.pdf', event);\">\n    Belief Update for Proper Epistemic Knowledge Bases.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://uqtmiller.github.io/publications/\">Miller, T.</a>;  and <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>International Joint Conference On Artificial Intelligence</i>,  2016. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.haz.ca/papers/miller-ijcai-16.pdf\"\n     onclick=\"log_download('miller-muise-beliefupdateforproperepistemicknowledgebases-2016', 'http://www.haz.ca/papers/miller-ijcai-16.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Belief Update for Proper Epistemic Knowledge Bases [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/miller-muise-beliefupdateforproperepistemicknowledgebases-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>10 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('miller-muise-beliefupdateforproperepistemicknowledgebases-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{miller-ijcai16,\n  title = {Belief Update for Proper Epistemic Knowledge Bases},\n  author = {Tim Miller and Christian Muise},\n  booktitle = {International Joint Conference On Artificial Intelligence},\n  year = {2016},\n  url = {http://www.haz.ca/papers/miller-ijcai-16.pdf},\n  keywords = {multi-agent planning, epistemic reasoning},\n  abstract={Reasoning about the nested beliefs of others is important in many multi-agent scenarios. While epistemic and doxastic logics lay a solid groundwork to approach such reasoning, the computational complexity of these logics is often too high for many tasks. Proper Epistemic Knowledge Bases (PEKBs) enforce two syntactic restrictions on formulae to obtain efficient querying: both disjunction and infinitely long nestings of modal operators are not permitted. PEKBs can be compiled, in exponential time, to a prime implicate formula that can be queried in polynomial time, while more recently, it was shown that consistent PEKBs had certain logical properties that meant this compilation was unnecessary, while still retaining polynomial-time querying. In this paper, we present a belief update mechanism for PEKBs that ensures the knowledge base remains consistent when new beliefs are added. This is achieved by first erasing any formulae that contradict these new beliefs. We show that this update mechanism can be computed in polynomial time, and we assess it against the well-known KM postulates for belief update.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Reasoning about the nested beliefs of others is important in many multi-agent scenarios. While epistemic and doxastic logics lay a solid groundwork to approach such reasoning, the computational complexity of these logics is often too high for many tasks. Proper Epistemic Knowledge Bases (PEKBs) enforce two syntactic restrictions on formulae to obtain efficient querying: both disjunction and infinitely long nestings of modal operators are not permitted. PEKBs can be compiled, in exponential time, to a prime implicate formula that can be queried in polynomial time, while more recently, it was shown that consistent PEKBs had certain logical properties that meant this compilation was unnecessary, while still retaining polynomial-time querying. In this paper, we present a belief update mechanism for PEKBs that ensures the knowledge base remains consistent when new beliefs are added. This is achieved by first erasing any formulae that contradict these new beliefs. We show that this update mechanism can be computed in polynomial time, and we assess it against the well-known KM postulates for belief update.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"muise-felli-miller-pearce-sonenberg-planningforasingleagentinamultiagentenvironmentusingfond-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.haz.ca/papers/muise-ijcai16.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'muise-felli-miller-pearce-sonenberg-planningforasingleagentinamultiagentenvironmentusingfond-2016', 'http://www.haz.ca/papers/muise-ijcai16.pdf', event);\">\n    Planning for a Single Agent in a Multi-Agent Environment Using FOND.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Felli, P.; <a class=\"bibbase author link\" href=\"https://uqtmiller.github.io/publications/\">Miller, T.</a>; Pearce, A. R.;  and Sonenberg, L.\n</span>\n\n  \n\n</span>\n\n  In <i>International Joint Conference On Artificial Intelligence</i>,  2016. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.haz.ca/papers/muise-ijcai16.pdf\"\n     onclick=\"log_download('muise-felli-miller-pearce-sonenberg-planningforasingleagentinamultiagentenvironmentusingfond-2016', 'http://www.haz.ca/papers/muise-ijcai16.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Planning for a Single Agent in a Multi-Agent Environment Using FOND [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-felli-miller-pearce-sonenberg-planningforasingleagentinamultiagentenvironmentusingfond-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>3 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-felli-miller-pearce-sonenberg-planningforasingleagentinamultiagentenvironmentusingfond-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{muise-ijcai16-mapasfond,\n  title = {Planning for a Single Agent in a Multi-Agent Environment Using FOND},\n  author = {Christian Muise and Paolo Felli and Tim Miller and Adrian R. Pearce and Liz Sonenberg},\n  booktitle = {International Joint Conference On Artificial Intelligence},\n  year = {2016},\n  url = {http://www.haz.ca/papers/muise-ijcai16.pdf},\n  keywords = {non-deterministic planning, multi-agent planning},\n  abstract={Single-agent planning in a multi-agent environment is challenging because the actions of other agents can affect our ability to achieve a goal. From a given agent&#x27;s perspective, actions of others can be viewed as non-deterministic outcomes of that agent&#x27;s actions. While simple conceptually, this interpretation of planning in a multi-agent environment as non-deterministic planning remains challenging, not only due to the non-determinism resulting from others&#x27; actions, but because it is not clear how to compactly model the possible actions of others in the environment. In this paper, we cast the problem of planning in a multi-agent environment as one of Fully-Observable Non-Deterministic (FOND) planning. We extend a non-deterministic planner to plan in a multi-agent setting, allowing non-deterministic planning technology to solve a new class of planning problems. To improve the efficiency in domains too large for solving optimally, we propose a technique to use the goals and possible actions of other agents to focus the search on a set of plausible actions. We evaluate our approach on existing and new multi-agent benchmarks, demonstrating that modelling the other agents&#x27; goals improves the quality of the resulting solutions.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Single-agent planning in a multi-agent environment is challenging because the actions of other agents can affect our ability to achieve a goal. From a given agent's perspective, actions of others can be viewed as non-deterministic outcomes of that agent's actions. While simple conceptually, this interpretation of planning in a multi-agent environment as non-deterministic planning remains challenging, not only due to the non-determinism resulting from others' actions, but because it is not clear how to compactly model the possible actions of others in the environment. In this paper, we cast the problem of planning in a multi-agent environment as one of Fully-Observable Non-Deterministic (FOND) planning. We extend a non-deterministic planner to plan in a multi-agent setting, allowing non-deterministic planning technology to solve a new class of planning problems. To improve the efficiency in domains too large for solving optimally, we propose a technique to use the goals and possible actions of other agents to focus the search on a set of plausible actions. We evaluate our approach on existing and new multi-agent benchmarks, demonstrating that modelling the other agents' goals improves the quality of the resulting solutions.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"lipovetzky-muise-geffner-trapsinvariantsanddeadends-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/13190\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'lipovetzky-muise-geffner-trapsinvariantsanddeadends-2016', 'http://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/13190', event);\">\n    Traps, Invariants, and Dead-Ends.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Lipovetzky, N.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>;  and Geffner, H.\n</span>\n\n  \n\n</span>\n\n  In <i>The 26th International Conference on Automated Planning and Scheduling</i>,  2016. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/13190\"\n     onclick=\"log_download('lipovetzky-muise-geffner-trapsinvariantsanddeadends-2016', 'http://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/13190', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Traps, Invariants, and Dead-Ends [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/lipovetzky-muise-geffner-trapsinvariantsanddeadends-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('lipovetzky-muise-geffner-trapsinvariantsanddeadends-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{lipovetzky-icaps16-trapper,\n  title = {Traps, Invariants, and Dead-Ends},\n  author = {Nir Lipovetzky and Christian Muise and Hector Geffner},\n  booktitle = {The 26th International Conference on Automated Planning and Scheduling},\n  year = {2016},\n  url = {http://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/13190},\n  keywords = {deadends, plan generation},\n  abstract={We consider the problem of deriving formulas that capture traps, invariants, and dead-ends in classical planning through polynomial forms of preprocessing. An invariant is a formula that is true in the initial state and in all reachable states. A trap is a conditional invariant: once a state is reached that makes the trap true, all the states that are reachable from it will satisfy the trap formula as well. Finally, dead-ends are formulas that are satisfied in states that make the goal unreachable. We introduce a preprocessing algorithm that computes traps in k-DNF form that is exponential in the k parameter, and show how the algorithm can be used to precompute invariants and dead-ends. We report also preliminary tests that illustrate the effectiveness of the preprocessing algorithm for identifying dead-end states, and compare it with the identification that follows from the use of the $h^1$ and $h^2$ heuristics that cannot be preprocessed, and must be computed at run time.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  We consider the problem of deriving formulas that capture traps, invariants, and dead-ends in classical planning through polynomial forms of preprocessing. An invariant is a formula that is true in the initial state and in all reachable states. A trap is a conditional invariant: once a state is reached that makes the trap true, all the states that are reachable from it will satisfy the trap formula as well. Finally, dead-ends are formulas that are satisfied in states that make the goal unreachable. We introduce a preprocessing algorithm that computes traps in k-DNF form that is exponential in the k parameter, and show how the algorithm can be used to precompute invariants and dead-ends. We report also preliminary tests that illustrate the effectiveness of the preprocessing algorithm for identifying dead-end states, and compare it with the identification that follows from the use of the $h^1$ and $h^2$ heuristics that cannot be preprocessed, and must be computed at run time.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"camacho-muise-mcilraith-fromfondtorobustprobabilisticplanningcomputingcompactpoliciesthatbypassavoidabledeadends-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/13188\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'camacho-muise-mcilraith-fromfondtorobustprobabilisticplanningcomputingcompactpoliciesthatbypassavoidabledeadends-2016', 'http://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/13188', event);\">\n    From FOND to Robust Probabilistic Planning: Computing compact policies that bypass avoidable deadends.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"http://www.albertocamacho.com/\">Camacho, A.</a>; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>;  and <a class=\"bibbase author link\" href=\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\">McIlraith, S. A.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>The 26th International Conference on Automated Planning and Scheduling</i>,  2016. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/13188\"\n     onclick=\"log_download('camacho-muise-mcilraith-fromfondtorobustprobabilisticplanningcomputingcompactpoliciesthatbypassavoidabledeadends-2016', 'http://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/13188', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"From FOND to Robust Probabilistic Planning: Computing compact policies that bypass avoidable deadends [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/camacho-muise-mcilraith-fromfondtorobustprobabilisticplanningcomputingcompactpoliciesthatbypassavoidabledeadends-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>6 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('camacho-muise-mcilraith-fromfondtorobustprobabilisticplanningcomputingcompactpoliciesthatbypassavoidabledeadends-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{camacho-icaps16-probprp,\n  title = {From FOND to Robust Probabilistic Planning: Computing compact policies that bypass avoidable deadends},\n  author = {Alberto Camacho and Christian Muise and Sheila A. McIlraith},\n  booktitle = {The 26th International Conference on Automated Planning and Scheduling},\n  year = {2016},\n  url = {http://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/13188},\n  keywords = {non-deterministic planning, probabilistic planning, plan generation},\n  abstract={We address the class of probabilistic planning problems where the objective is to maximize the probability of reaching a prescribed goal. The complexity of probabilistic planning problems makes it difficult to compute high quality solutions for large instances, and existing algorithms either do not scale, or do so at the expense of the solution quality. We leverage core similarities between probabilistic and fully observable non-deterministic (FOND) planning to construct a sound, offline probabilistic planner, ProbPRP, that exploits algorithmic advances from state-of-the-art FOND planner, PRP, to compute compact policies that are guaranteed to bypass avoidable deadends. We evaluate ProbPRP on a selection of benchmarks used in past probabilistic planning competitions. The results show that ProbPRP, in many cases, outperforms the state of the art, computing substantially more robust policies and at times doing so orders of magnitude faster.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  We address the class of probabilistic planning problems where the objective is to maximize the probability of reaching a prescribed goal. The complexity of probabilistic planning problems makes it difficult to compute high quality solutions for large instances, and existing algorithms either do not scale, or do so at the expense of the solution quality. We leverage core similarities between probabilistic and fully observable non-deterministic (FOND) planning to construct a sound, offline probabilistic planner, ProbPRP, that exploits algorithmic advances from state-of-the-art FOND planner, PRP, to compute compact policies that are guaranteed to bypass avoidable deadends. We evaluate ProbPRP on a selection of benchmarks used in past probabilistic planning competitions. The results show that ProbPRP, in many cases, outperforms the state of the art, computing substantially more robust policies and at times doing so orders of magnitude faster.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"miller-muise-felli-pearce-sonenberg-knowingwhetherinproperepistemicknowledgebases-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.haz.ca/papers/miller-aaai-16.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'miller-muise-felli-pearce-sonenberg-knowingwhetherinproperepistemicknowledgebases-2016', 'http://www.haz.ca/papers/miller-aaai-16.pdf', event);\">\n    'Knowing Whether' in Proper Epistemic Knowledge Bases.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Miller, T.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Felli, P.; Pearce, A. R.;  and Sonenberg, L.\n</span>\n\n  \n\n</span>\n\n  In <i>The 30th AAAI Conference on Artificial Intelligence</i>,  2016. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.haz.ca/papers/miller-aaai-16.pdf\"\n     onclick=\"log_download('miller-muise-felli-pearce-sonenberg-knowingwhetherinproperepistemicknowledgebases-2016', 'http://www.haz.ca/papers/miller-aaai-16.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"&#x27;Knowing Whether&#x27; in Proper Epistemic Knowledge Bases [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/miller-muise-felli-pearce-sonenberg-knowingwhetherinproperepistemicknowledgebases-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>3 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('miller-muise-felli-pearce-sonenberg-knowingwhetherinproperepistemicknowledgebases-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{miller-aaai-16,\n  author = {Tim Miller and Christian Muise and Paolo Felli and Adrian R. Pearce and Liz Sonenberg},\n  year = {2016},\n  booktitle = {The 30th AAAI Conference on Artificial Intelligence},\n  keywords = {multi-agent planning, epistemic reasoning},\n  url = {http://www.haz.ca/papers/miller-aaai-16.pdf},\n  title = {&#x27;Knowing Whether&#x27; in Proper Epistemic Knowledge Bases},\n  abstract = {Proper epistemic knowledge bases (PEKBs) are syntactic knowledge bases that use multi-agent epistemic logic to represent nested multi-agent knowledge and belief. PEKBs have certain syntactic restrictions that lead to desirable computational properties; primarily, a PEKB is a conjunction of modal literals, and therefore contains no disjunction. Sound entailment can be checked in polynomial time, and is complete for a large set of arbitrary formulae in logics Kn and KDn. In this paper, we extend PEKBs to deal with a restricted form of disjunction: ‘knowing whether’. An agent i knows whether ϕ iff agent i knows ϕ or knows ¬ϕ; that is, iϕ ∨ i¬ϕ. In our experience, the ability to represent that an agent knows whether something holds is useful in many multi-agent domains. We represent knowing whether with a modal operator, ∆i, and present sound polynomial-time entailment algorithms on PEKBs with ∆i in Kn and KDn, but which are complete for a smaller class of queries than standard PEKBs.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Proper epistemic knowledge bases (PEKBs) are syntactic knowledge bases that use multi-agent epistemic logic to represent nested multi-agent knowledge and belief. PEKBs have certain syntactic restrictions that lead to desirable computational properties; primarily, a PEKB is a conjunction of modal literals, and therefore contains no disjunction. Sound entailment can be checked in polynomial time, and is complete for a large set of arbitrary formulae in logics Kn and KDn. In this paper, we extend PEKBs to deal with a restricted form of disjunction: ‘knowing whether’. An agent i knows whether ϕ iff agent i knows ϕ or knows ¬ϕ; that is, iϕ ∨ i¬ϕ. In our experience, the ability to represent that an agent knows whether something holds is useful in many multi-agent domains. We represent knowing whether with a modal operator, ∆i, and present sound polynomial-time entailment algorithms on PEKBs with ∆i in Kn and KDn, but which are complete for a smaller class of queries than standard PEKBs.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"sonenberg-miller-pearce-felli-muise-dignum-socialplanningforsocialhri-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://arxiv.org/pdf/1602.06483v1.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'sonenberg-miller-pearce-felli-muise-dignum-socialplanningforsocialhri-2016', 'http://arxiv.org/pdf/1602.06483v1.pdf', event);\">\n    Social planning for social HRI.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Sonenberg, L.; Miller, T.; Pearce, A.; Felli, P.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>;  and Dignum, F.\n</span>\n\n  \n\n</span>\n\n  In <i>2nd Workshop on Cognitive Architectures for Social Human-Robot Interaction</i>,  2016. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://arxiv.org/pdf/1602.06483v1.pdf\"\n     onclick=\"log_download('sonenberg-miller-pearce-felli-muise-dignum-socialplanningforsocialhri-2016', 'http://arxiv.org/pdf/1602.06483v1.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Social planning for social HRI [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/sonenberg-miller-pearce-felli-muise-dignum-socialplanningforsocialhri-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('sonenberg-miller-pearce-felli-muise-dignum-socialplanningforsocialhri-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{sonenberg-cashri16,\n  author = {Liz Sonenberg and Tim Miller and Adrian Pearce and Paolo Felli and Christian Muise and Frank Dignum},\n  title = {Social planning for social HRI},\n  booktitle = {2nd Workshop on Cognitive Architectures for Social Human-Robot Interaction},\n  year = {2016},\n  keywords = {human-agent collaboration},\n  url = {http://arxiv.org/pdf/1602.06483v1.pdf}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"muise-mcilraith-beck-hsu-dsharpfastddnnfcompilationwithsharpsatamendedversion-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://haz.ca/dsharp-related.html\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'muise-mcilraith-beck-hsu-dsharpfastddnnfcompilationwithsharpsatamendedversion-2016', 'http://haz.ca/dsharp-related.html', event);\">\n    DSHARP: Fast d-DNNF Compilation with sharpSAT (Amended Version).\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; <a class=\"bibbase author link\" href=\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\">McIlraith, S. A.</a>; Beck, J. C.;  and Hsu, E.\n</span>\n\n  \n\n</span>\n\n  In <i>AAAI-16 Workshop on Beyond NP</i>,  2016. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://haz.ca/dsharp-related.html\"\n     onclick=\"log_download('muise-mcilraith-beck-hsu-dsharpfastddnnfcompilationwithsharpsatamendedversion-2016', 'http://haz.ca/dsharp-related.html', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"DSHARP: Fast d-DNNF Compilation with sharpSAT (Amended Version) [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-mcilraith-beck-hsu-dsharpfastddnnfcompilationwithsharpsatamendedversion-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-mcilraith-beck-hsu-dsharpfastddnnfcompilationwithsharpsatamendedversion-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{muise-beyondnp16-dsharp,\nauthor = {Muise, Christian and McIlraith, Sheila A. and Beck, J. Christopher and Hsu, Eric},\nbooktitle = {AAAI-16 Workshop on Beyond NP},\ntitle = {{DSHARP: Fast d-DNNF Compilation with sharpSAT (Amended Version)}},\nkeywords = {sat, knowledge compilation},\nurl = {http://haz.ca/dsharp-related.html},\nyear = {2016}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2015\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2015')\"\n      onkeypress=\"toggleGroup('group_2015')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2015</span>\n      <span class=\"bibbase_group_count\">\n        \n        (11)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"aziz-chu-muise-stuckey-projectedmodelcounting-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://arxiv.org/pdf/1507.07648v1.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'aziz-chu-muise-stuckey-projectedmodelcounting-2015', 'http://arxiv.org/pdf/1507.07648v1.pdf', event);\">\n    Projected Model Counting.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Aziz, R. A.; Chu, G.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>;  and Stuckey, P.\n</span>\n\n  \n\n</span>\n\n  In <i>International Conference on Theory and Applications of Satisfiability Testing</i>, Austin, USA,  2015. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://arxiv.org/pdf/1507.07648v1.pdf\"\n     onclick=\"log_download('aziz-chu-muise-stuckey-projectedmodelcounting-2015', 'http://arxiv.org/pdf/1507.07648v1.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Projected Model Counting [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/aziz-chu-muise-stuckey-projectedmodelcounting-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('aziz-chu-muise-stuckey-projectedmodelcounting-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{Aziz_CSM_15,\n  author = {Rehan Abdul Aziz and Geoffrey Chu and Christian Muise and Peter Stuckey},\n  year = {2015},\n  booktitle = {International Conference on Theory and Applications of Satisfiability Testing},\n  address = &quot;Austin, {USA}&quot;,\n  keywords = {sat, knowledge compilation},\n  title = {Projected Model Counting},\n  url = {http://arxiv.org/pdf/1507.07648v1.pdf},\n  abstract = {Model counting is the task of computing the number of assignments to variables V that satisfy a given propositional theory F. The model counting problem is denoted as #SAT. Model counting is an essential tool in probabilistic reasoning. In this paper, we introduce the problem of model counting projected on a subset of original variables that we call priority variables P ⊆ V. The task is to compute the number of assignments to P such that there exists an extension to non-priority variables V \\ P that satisfies F. We denote this as #∃SAT. Projected model counting arises when some parts of the model are irrelevant to the counts, in particular when we require additional variables to model the problem we are counting in SAT. We discuss three different approaches to #∃SAT (two of which are novel), and compare their performance on different benchmark problems.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Model counting is the task of computing the number of assignments to variables V that satisfy a given propositional theory F. The model counting problem is denoted as #SAT. Model counting is an essential tool in probabilistic reasoning. In this paper, we introduce the problem of model counting projected on a subset of original variables that we call priority variables P ⊆ V. The task is to compute the number of assignments to P such that there exists an extension to non-priority variables V \\ P that satisfies F. We denote this as #∃SAT. Projected model counting arises when some parts of the model are irrelevant to the counts, in particular when we require additional variables to model the problem we are counting in SAT. We discuss three different approaches to #∃SAT (two of which are novel), and compare their performance on different benchmark problems.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"muise-lipovetzky-ramirez-maplapktomnipotentmultiagentplanningviacompilationtoclassicalplanning-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.haz.ca/papers/muise_CoDMAP15.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'muise-lipovetzky-ramirez-maplapktomnipotentmultiagentplanningviacompilationtoclassicalplanning-2015', 'http://www.haz.ca/papers/muise_CoDMAP15.pdf', event);\">\n    MAP-LAPKT: Omnipotent Multi-Agent Planning via Compilation to Classical Planning.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Lipovetzky, N.;  and Ramirez, M.\n</span>\n\n  \n\n</span>\n\n  In <i>Competition of Distributed and Multiagent Planners</i>,  2015. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.haz.ca/papers/muise_CoDMAP15.pdf\"\n     onclick=\"log_download('muise-lipovetzky-ramirez-maplapktomnipotentmultiagentplanningviacompilationtoclassicalplanning-2015', 'http://www.haz.ca/papers/muise_CoDMAP15.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"MAP-LAPKT: Omnipotent Multi-Agent Planning via Compilation to Classical Planning [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-lipovetzky-ramirez-maplapktomnipotentmultiagentplanningviacompilationtoclassicalplanning-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-lipovetzky-ramirez-maplapktomnipotentmultiagentplanningviacompilationtoclassicalplanning-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{muise-codmap15,\n  title = {{MAP-LAPKT}: Omnipotent Multi-Agent Planning via Compilation to Classical Planning},\n  author = {Christian Muise and Nir Lipovetzky and Miquel Ramirez},\n  booktitle = {Competition of Distributed and Multiagent Planners},\n  year = {2015},\n  keywords = {multi-agent planning},\n  url = {http://www.haz.ca/papers/muise_CoDMAP15.pdf}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"muise-lipovetzky-unplannabilityipctrack-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.haz.ca/papers/muise_WIPC15.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'muise-lipovetzky-unplannabilityipctrack-2015', 'http://www.haz.ca/papers/muise_WIPC15.pdf', event);\">\n    Unplannability IPC Track.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>;  and Lipovetzky, N.\n</span>\n\n  \n\n</span>\n\n  In <i>Workshop on the International Planning Competition</i>,  2015. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.haz.ca/papers/muise_WIPC15.pdf\"\n     onclick=\"log_download('muise-lipovetzky-unplannabilityipctrack-2015', 'http://www.haz.ca/papers/muise_WIPC15.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Unplannability IPC Track [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-lipovetzky-unplannabilityipctrack-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-lipovetzky-unplannabilityipctrack-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{muise-wipc15,\n  title = {Unplannability IPC Track},\n  author = {Christian Muise and Nir Lipovetzky},\n  booktitle = {Workshop on the International Planning Competition},\n  year = {2015},\n  keywords = {unsolvability,deadends},\n  url = {http://www.haz.ca/papers/muise_WIPC15.pdf},\n  abstract = {The majority of research in the field of automated planning focuses on the synthesis of plans for problems that are solvable. We propose an IPC track to focus on the important and understudied area of unplannibility: proving that a planning problem is unsolvable. We will focus on classical planning problems, as methods for determining whether or not unplannability can have wider applications for classical planning problems (e.g., recognizing and avoiding deadends in the state space) as well as solving planning problems with uncertainty (e.g., identifying when a deterministic approximation of the problem is unsolvable). The unplannability track follows similar contests in other fields; for example, the UNSAT track for the field of Boolean Satisfiability. In a similar vein, we hope that the introduction of an unplannability track will foster new innovation for techniques dedicated to identifying planning problems that cannot be solved.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The majority of research in the field of automated planning focuses on the synthesis of plans for problems that are solvable. We propose an IPC track to focus on the important and understudied area of unplannibility: proving that a planning problem is unsolvable. We will focus on classical planning problems, as methods for determining whether or not unplannability can have wider applications for classical planning problems (e.g., recognizing and avoiding deadends in the state space) as well as solving planning problems with uncertainty (e.g., identifying when a deterministic approximation of the problem is unsolvable). The unplannability track follows similar contests in other fields; for example, the UNSAT track for the field of Boolean Satisfiability. In a similar vein, we hope that the introduction of an unplannability track will foster new innovation for techniques dedicated to identifying planning problems that cannot be solved.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"muise-dignum-felli-miller-pearce-sonenberg-towardsteamformationviaautomatedplanning-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.haz.ca/papers/muise-coinATijcai-15.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'muise-dignum-felli-miller-pearce-sonenberg-towardsteamformationviaautomatedplanning-2015', 'http://www.haz.ca/papers/muise-coinATijcai-15.pdf', event);\">\n    Towards Team Formation via Automated Planning.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Dignum, F.; Felli, P.; <a class=\"bibbase author link\" href=\"https://uqtmiller.github.io/publications/\">Miller, T.</a>; Pearce, A. R.;  and Sonenberg, L.\n</span>\n\n  \n\n</span>\n\n  In <i>International Workshop on Coordination, Organisation, Institutions and Norms in Multi-Agent Systems</i>,  2015. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.haz.ca/papers/muise-coinATijcai-15.pdf\"\n     onclick=\"log_download('muise-dignum-felli-miller-pearce-sonenberg-towardsteamformationviaautomatedplanning-2015', 'http://www.haz.ca/papers/muise-coinATijcai-15.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Towards Team Formation via Automated Planning [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-dignum-felli-miller-pearce-sonenberg-towardsteamformationviaautomatedplanning-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-dignum-felli-miller-pearce-sonenberg-towardsteamformationviaautomatedplanning-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{muise-coin15-teamwork,\n  title = {Towards Team Formation via Automated Planning},\n  author = {Christian Muise and Frank Dignum and Paolo Felli and Tim Miller and Adrian R. Pearce and Liz Sonenberg},\n  booktitle = {International Workshop on Coordination, Organisation, Institutions and Norms in Multi-Agent Systems},\n  year = {2015},\n  keywords = {multi-agent planning, epistemic reasoning},\n  url = {http://www.haz.ca/papers/muise-coinATijcai-15.pdf},\n  abstract={Cooperative problem solving involves four key phases: (1) finding potential members to form a team, (2) forming the team, (3) formulating a plan for the team, and (4) executing the plan. We extend recent work on multi-agent epistemic planning and apply it to the problem of team formation in a blocksworld scenario. We provide an encoding of the first three phases of team formation from the perspective of an initiator, and show how automated planning efficiently yields conditional plans that guarantee certain collective intentions will be achieved. The expressiveness of the epistemic planning formalism, which supports modelling with the nested beliefs of agents, opens the prospect of broad applicability to the operationalisation of collective intention.}\n}\n\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Cooperative problem solving involves four key phases: (1) finding potential members to form a team, (2) forming the team, (3) formulating a plan for the team, and (4) executing the plan. We extend recent work on multi-agent epistemic planning and apply it to the problem of team formation in a blocksworld scenario. We provide an encoding of the first three phases of team formation from the perspective of an initiator, and show how automated planning efficiently yields conditional plans that guarantee certain collective intentions will be achieved. The expressiveness of the epistemic planning formalism, which supports modelling with the nested beliefs of agents, opens the prospect of broad applicability to the operationalisation of collective intention.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"camacho-muise-ganeshen-mcilraith-fromfondtoprobabilisticplanningguidingsearchforqualitypolicies-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.haz.ca/papers/camacho-hsdip15-probprp.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'camacho-muise-ganeshen-mcilraith-fromfondtoprobabilisticplanningguidingsearchforqualitypolicies-2015', 'http://www.haz.ca/papers/camacho-hsdip15-probprp.pdf', event);\">\n    From FOND to Probabilistic Planning: Guiding search for quality policies.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"http://www.albertocamacho.com/\">Camacho, A.</a>; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Ganeshen, A.;  and <a class=\"bibbase author link\" href=\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\">McIlraith, S. A.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>Workshop on Heuristic Search and Domain Independent Planning (HSDIP'15)</i>,  2015. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.haz.ca/papers/camacho-hsdip15-probprp.pdf\"\n     onclick=\"log_download('camacho-muise-ganeshen-mcilraith-fromfondtoprobabilisticplanningguidingsearchforqualitypolicies-2015', 'http://www.haz.ca/papers/camacho-hsdip15-probprp.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"From FOND to Probabilistic Planning: Guiding search for quality policies [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/camacho-muise-ganeshen-mcilraith-fromfondtoprobabilisticplanningguidingsearchforqualitypolicies-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('camacho-muise-ganeshen-mcilraith-fromfondtoprobabilisticplanningguidingsearchforqualitypolicies-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{camacho-hsdip15-probprp,\n  title = {From FOND to Probabilistic Planning: Guiding search for quality policies},\n  author = {Alberto Camacho and Christian Muise and Akshay Ganeshen and Sheila A. McIlraith},\n  booktitle = {Workshop on Heuristic Search and Domain Independent Planning ({HSDIP}&#x27;15)},\n  year = {2015},\n  keywords = {non-deterministic planning, probabilistic planning, plan generation},\n  url={http://www.haz.ca/papers/camacho-hsdip15-probprp.pdf},\n  abstract={We address the class of probabilistic planning problems where the objective is to maximize the probability of reaching a prescribed goal (MAXPROB). State-of-the-art probabilistic planners, and in particular MAXPROB planners, offer few guarantees with respect to the quality or optimality of the solutions that they find. The complexity of MAXPROB problems makes it difficult to compute high quality solutions for big problems, and existing algorithms either do not scale well, or provide poor quality solutions. We exploit core similarities between probabilistic and fully observable non-deterministic (FOND) planning models to extend the state-of-the-art FOND planner, PRP, to be a sound and sometimes complete MAXPROB solver that is guaranteed to sidestep avoidable deadends. We evaluate our planner, ProbPRP, on a selection of benchmarks used in past probabilistic planning competitions. The results show that ProbPRP outperforms previous state-of-the-art algorithms for solving MAXPROB, and computes substantially more robust policies, at times doing so orders of magnitude faster.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  We address the class of probabilistic planning problems where the objective is to maximize the probability of reaching a prescribed goal (MAXPROB). State-of-the-art probabilistic planners, and in particular MAXPROB planners, offer few guarantees with respect to the quality or optimality of the solutions that they find. The complexity of MAXPROB problems makes it difficult to compute high quality solutions for big problems, and existing algorithms either do not scale well, or provide poor quality solutions. We exploit core similarities between probabilistic and fully observable non-deterministic (FOND) planning models to extend the state-of-the-art FOND planner, PRP, to be a sound and sometimes complete MAXPROB solver that is guaranteed to sidestep avoidable deadends. We evaluate our planner, ProbPRP, on a selection of benchmarks used in past probabilistic planning competitions. The results show that ProbPRP outperforms previous state-of-the-art algorithms for solving MAXPROB, and computes substantially more robust policies, at times doing so orders of magnitude faster.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"muise-belle-felli-mcilraith-miller-pearce-sonenberg-planningovermultiagentepistemicstatesaclassicalplanningapproachamendedversion-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.haz.ca/papers/muise-dmap15-pdkbplanning.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'muise-belle-felli-mcilraith-miller-pearce-sonenberg-planningovermultiagentepistemicstatesaclassicalplanningapproachamendedversion-2015', 'http://www.haz.ca/papers/muise-dmap15-pdkbplanning.pdf', event);\">\n    Planning Over Multi-Agent Epistemic States: A Classical Planning Approach (Amended Version).\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; <a class=\"bibbase author link\" href=\"https://bibbase.org/show?bib=https://dblp.org/pid/52/570.bib\">Belle, V.</a>; Felli, P.; <a class=\"bibbase author link\" href=\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\">McIlraith, S. A.</a>; Miller, T.; Pearce, A. R.;  and Sonenberg, L.\n</span>\n\n  \n\n</span>\n\n  In <i>Workshop on Distributed and Multi-Agent Planning (DMAP'15)</i>,  2015. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.haz.ca/papers/muise-dmap15-pdkbplanning.pdf\"\n     onclick=\"log_download('muise-belle-felli-mcilraith-miller-pearce-sonenberg-planningovermultiagentepistemicstatesaclassicalplanningapproachamendedversion-2015', 'http://www.haz.ca/papers/muise-dmap15-pdkbplanning.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Planning Over Multi-Agent Epistemic States: A Classical Planning Approach (Amended Version) [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-belle-felli-mcilraith-miller-pearce-sonenberg-planningovermultiagentepistemicstatesaclassicalplanningapproachamendedversion-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>6 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-belle-felli-mcilraith-miller-pearce-sonenberg-planningovermultiagentepistemicstatesaclassicalplanningapproachamendedversion-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{muise-dmap15-pdkbplanning,\n  title = {Planning Over Multi-Agent Epistemic States: A Classical Planning Approach (Amended Version)},\n  author = {Christian Muise and Vaishak Belle and Paolo Felli and Sheila A. McIlraith and Tim Miller and Adrian R. Pearce and Liz Sonenberg},\n  booktitle = {Workshop on Distributed and Multi-Agent Planning ({DMAP}&#x27;15)},\n  year = {2015},\n  keywords = {multi-agent planning, epistemic reasoning},\n  url={http://www.haz.ca/papers/muise-dmap15-pdkbplanning.pdf},\n  abstract={Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology. Our approach represents an important first step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology. Our approach represents an important first step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"muise-felli-miller-pearce-sonenberg-leveragingfondplanningtechnologytosolvemultiagentplanningproblems-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.haz.ca/papers/muise-dmap15-mapasfond.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'muise-felli-miller-pearce-sonenberg-leveragingfondplanningtechnologytosolvemultiagentplanningproblems-2015', 'http://www.haz.ca/papers/muise-dmap15-mapasfond.pdf', event);\">\n    Leveraging FOND Planning Technology to Solve Multi-Agent Planning Problems.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Felli, P.; <a class=\"bibbase author link\" href=\"https://uqtmiller.github.io/publications/\">Miller, T.</a>; Pearce, A. R.;  and Sonenberg, L.\n</span>\n\n  \n\n</span>\n\n  In <i>Workshop on Distributed and Multi-Agent Planning (DMAP'15)</i>,  2015. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.haz.ca/papers/muise-dmap15-mapasfond.pdf\"\n     onclick=\"log_download('muise-felli-miller-pearce-sonenberg-leveragingfondplanningtechnologytosolvemultiagentplanningproblems-2015', 'http://www.haz.ca/papers/muise-dmap15-mapasfond.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Leveraging FOND Planning Technology to Solve Multi-Agent Planning Problems [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-felli-miller-pearce-sonenberg-leveragingfondplanningtechnologytosolvemultiagentplanningproblems-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-felli-miller-pearce-sonenberg-leveragingfondplanningtechnologytosolvemultiagentplanningproblems-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{muise-dmap15-mapasfond,\n  title = {Leveraging FOND Planning Technology to Solve Multi-Agent Planning Problems},\n  author = {Christian Muise and Paolo Felli and Tim Miller and Adrian R. Pearce and Liz Sonenberg},\n  booktitle = {Workshop on Distributed and Multi-Agent Planning ({DMAP}&#x27;15)},\n  year = {2015},\n  keywords = {non-deterministic planning, multi-agent planning},\n  url={http://www.haz.ca/papers/muise-dmap15-mapasfond.pdf},\n  abstract={Single-agent planning in a multi-agent environment is challenging because the actions of other agents can affect our ability to achieve a goal. From a given agent&#x27;s perspective, actions of others can be viewed as non-deterministic outcomes of that agent&#x27;s actions. While simple conceptually, this interpretation of planning in a multi-agent environment as non-deterministic planning is challenging due to the non-determinism resulting from others&#x27; actions, and because it is not clear how to compactly model the possible actions of others in the environment. In this paper, we cast the problem of planning in a multi-agent environment as one of Fully-Observable Non-Deterministic (FOND) planning. We extend a non-deterministic planner to plan in a multi-agent setting, given the goals and possible actions of other agents. We use the other agents&#x27; goals to reduce their set of possible actions to a set of plausible actions, allowing non-deterministic planning technology to solve a new class of planning problems in first-person multi-agent environments. We demonstrate our approach on new and existing multi-agent benchmarks, demonstrating that modelling the other agents&#x27; goals reduces complexity.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Single-agent planning in a multi-agent environment is challenging because the actions of other agents can affect our ability to achieve a goal. From a given agent's perspective, actions of others can be viewed as non-deterministic outcomes of that agent's actions. While simple conceptually, this interpretation of planning in a multi-agent environment as non-deterministic planning is challenging due to the non-determinism resulting from others' actions, and because it is not clear how to compactly model the possible actions of others in the environment. In this paper, we cast the problem of planning in a multi-agent environment as one of Fully-Observable Non-Deterministic (FOND) planning. We extend a non-deterministic planner to plan in a multi-agent setting, given the goals and possible actions of other agents. We use the other agents' goals to reduce their set of possible actions to a set of plausible actions, allowing non-deterministic planning technology to solve a new class of planning problems in first-person multi-agent environments. We demonstrate our approach on new and existing multi-agent benchmarks, demonstrating that modelling the other agents' goals reduces complexity.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"felli-miller-muise-pearce-sonenberg-computingsocialbehavioursusingagentmodels-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.haz.ca/papers/felli-ijcai15.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'felli-miller-muise-pearce-sonenberg-computingsocialbehavioursusingagentmodels-2015', 'http://www.haz.ca/papers/felli-ijcai15.pdf', event);\">\n    Computing Social Behaviours Using Agent Models.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Felli, P.; <a class=\"bibbase author link\" href=\"https://uqtmiller.github.io/publications/\">Miller, T.</a>; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Pearce, A. R.;  and Sonenberg, L.\n</span>\n\n  \n\n</span>\n\n  In <i>International Joint Conference on Artificial Intelligence, IJCAI 2015</i>,  2015. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.haz.ca/papers/felli-ijcai15.pdf\"\n     onclick=\"log_download('felli-miller-muise-pearce-sonenberg-computingsocialbehavioursusingagentmodels-2015', 'http://www.haz.ca/papers/felli-ijcai15.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Computing Social Behaviours Using Agent Models [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/felli-miller-muise-pearce-sonenberg-computingsocialbehavioursusingagentmodels-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('felli-miller-muise-pearce-sonenberg-computingsocialbehavioursusingagentmodels-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{felli-ijcai-2015,\n  title = {Computing Social Behaviours Using Agent Models},\n  author = {Felli, Paolo and Miller, Tim and Muise, Christian and Pearce, Adrian R. and Sonenberg, Liz},\n  booktitle = {International Joint Conference on Artificial Intelligence, {IJCAI} 2015},\n  year = {2015},\n  keywords = {human-agent collaboration},\n  url={http://www.haz.ca/papers/felli-ijcai15.pdf},\n  abstract = {Agents can be thought of as following a social behaviour, depending on the context in which they are interacting. We devise a computationally grounded mechanism to represent and reason about others in social terms, reflecting the local perspective of an agent (first-person view), to support both stereotypical and empathetic reasoning. We use a hierarchy of agent models to discriminate which behaviours of others are plausible, and decide which behaviour for ourselves is socially acceptable, i.e. conforms to the social context. To this aim, we investigate the implications of considering agents capable of various degrees of Theory of Mind, and discuss a scenario showing how this affects behaviour.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Agents can be thought of as following a social behaviour, depending on the context in which they are interacting. We devise a computationally grounded mechanism to represent and reason about others in social terms, reflecting the local perspective of an agent (first-person view), to support both stereotypical and empathetic reasoning. We use a hierarchy of agent models to discriminate which behaviours of others are plausible, and decide which behaviour for ourselves is socially acceptable, i.e. conforms to the social context. To this aim, we investigate the implications of considering agents capable of various degrees of Theory of Mind, and discuss a scenario showing how this affects behaviour.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"muise-miller-felli-pearce-sonenberg-efficientreasoningwithconsistentproperepistemicknowledgebases-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.haz.ca/papers/muise-aamas-15.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'muise-miller-felli-pearce-sonenberg-efficientreasoningwithconsistentproperepistemicknowledgebases-2015', 'http://www.haz.ca/papers/muise-aamas-15.pdf', event);\">\n    Efficient Reasoning With Consistent Proper Epistemic Knowledge Bases.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; <a class=\"bibbase author link\" href=\"https://uqtmiller.github.io/publications/\">Miller, T.</a>; Felli, P.; Pearce, A. R.;  and Sonenberg, L.\n</span>\n\n  \n\n</span>\n\n  In <i>The International Conference on Autonomous Agents and Multiagent Systems</i>,  2015. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.haz.ca/papers/muise-aamas-15.pdf\"\n     onclick=\"log_download('muise-miller-felli-pearce-sonenberg-efficientreasoningwithconsistentproperepistemicknowledgebases-2015', 'http://www.haz.ca/papers/muise-aamas-15.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Efficient Reasoning With Consistent Proper Epistemic Knowledge Bases [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-miller-felli-pearce-sonenberg-efficientreasoningwithconsistentproperepistemicknowledgebases-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>3 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-miller-felli-pearce-sonenberg-efficientreasoningwithconsistentproperepistemicknowledgebases-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{muise-aamas-15,\n  abstract={Reasoning about the nested beliefs or knowledge of other agents is essential for many collaborative and competitive tasks. However, reasoning with nested belief (for example through epistemic logics) is computationally expensive. Proper Epistemic Knowledge Bases (PEKBs) address this by enforcing syntactic restrictions on the knowledge base. By compiling a PEKB and query formula into a specific normal form, entailment can be checked in polynomial time, which is sound and complete for the epistemic logic Kn. The downside is that the complexity of compiling into the normal form is exponential in time and space. In this work, we extend PEKBs to handle belief in the logic of Kd. We show that this simplifies the complexity of the required reasoning, and importantly, achieves polynomial entailment checking without first having to compile the PEKB into a normal form. Also, we present an alternative approach that calculates the closure of a PEKB, which is exponential in the maximum depth of nested belief, but for which entailment checking is constant on average.},\n  title={Efficient Reasoning With Consistent Proper Epistemic Knowledge Bases},\n  author={Christian Muise and Tim Miller and Paolo Felli and Adrian R. Pearce and Liz Sonenberg},\n  booktitle={The International Conference on Autonomous Agents and Multiagent Systems},\n  year={2015},\n  keywords = {multi-agent planning, epistemic reasoning},\n  url={http://www.haz.ca/papers/muise-aamas-15.pdf}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Reasoning about the nested beliefs or knowledge of other agents is essential for many collaborative and competitive tasks. However, reasoning with nested belief (for example through epistemic logics) is computationally expensive. Proper Epistemic Knowledge Bases (PEKBs) address this by enforcing syntactic restrictions on the knowledge base. By compiling a PEKB and query formula into a specific normal form, entailment can be checked in polynomial time, which is sound and complete for the epistemic logic Kn. The downside is that the complexity of compiling into the normal form is exponential in time and space. In this work, we extend PEKBs to handle belief in the logic of Kd. We show that this simplifies the complexity of the required reasoning, and importantly, achieves polynomial entailment checking without first having to compile the PEKB into a normal form. Also, we present an alternative approach that calculates the closure of a PEKB, which is exponential in the maximum depth of nested belief, but for which entailment checking is constant on average.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"aziz-chu-muise-stuckey-stablemodelcountinganditsapplicationinprobabilisticlogicprogramming-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://arxiv.org/pdf/1411.5410v1.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'aziz-chu-muise-stuckey-stablemodelcountinganditsapplicationinprobabilisticlogicprogramming-2015', 'http://arxiv.org/pdf/1411.5410v1.pdf', event);\">\n    Stable model counting and its application in probabilistic logic programming.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Aziz, R. A.; Chu, G.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>;  and Stuckey, P.\n</span>\n\n  \n\n</span>\n\n  In <i>The 29th AAAI Conference on Artificial Intelligence</i>,  2015. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://arxiv.org/pdf/1411.5410v1.pdf\"\n     onclick=\"log_download('aziz-chu-muise-stuckey-stablemodelcountinganditsapplicationinprobabilisticlogicprogramming-2015', 'http://arxiv.org/pdf/1411.5410v1.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Stable model counting and its application in probabilistic logic programming [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/aziz-chu-muise-stuckey-stablemodelcountinganditsapplicationinprobabilisticlogicprogramming-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('aziz-chu-muise-stuckey-stablemodelcountinganditsapplicationinprobabilisticlogicprogramming-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{aziz-aaai-15,\n  abstract={Model counting is the problem of computing the number of models that satisfy a given propositional theory. It has recently been applied to solving inference tasks in probabilistic logic programming, where the goal is to compute the probability of given queries being true provided a set of mutually independent random variables, a model (a logic program) and some evidence. The core of solving this inference task involves translating the logic program to a propositional theory and using a model counter. In this paper, we show that for some problems that involve inductive definitions like reachability in a graph, the translation of logic programs to SAT can be expensive for the purpose of solving inference tasks. For such problems, direct implementation of stable model semantics allows for more efficient solving. We present two implementation techniques, based on unfounded set detection, that extend a propositional model counter to a stable model counter. Our experiments show that for particular problems, our approach can outperform a state-of-the-art probabilistic logic programming solver by several orders of magnitude in terms of running time and space requirements, and can solve instances of significantly larger sizes on which the current solver runs out of time or memory.},\n  title={Stable model counting and its application in probabilistic logic programming},\n  author={Rehan Abdul Aziz and Geoffrey Chu and Christian Muise and Peter Stuckey},\n  booktitle={The 29th AAAI Conference on Artificial Intelligence},\n  keywords = {sat, knowledge compilation},\n  year={2015},\n  url={http://arxiv.org/pdf/1411.5410v1.pdf}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Model counting is the problem of computing the number of models that satisfy a given propositional theory. It has recently been applied to solving inference tasks in probabilistic logic programming, where the goal is to compute the probability of given queries being true provided a set of mutually independent random variables, a model (a logic program) and some evidence. The core of solving this inference task involves translating the logic program to a propositional theory and using a model counter. In this paper, we show that for some problems that involve inductive definitions like reachability in a graph, the translation of logic programs to SAT can be expensive for the purpose of solving inference tasks. For such problems, direct implementation of stable model semantics allows for more efficient solving. We present two implementation techniques, based on unfounded set detection, that extend a propositional model counter to a stable model counter. Our experiments show that for particular problems, our approach can outperform a state-of-the-art probabilistic logic programming solver by several orders of magnitude in terms of running time and space requirements, and can solve instances of significantly larger sizes on which the current solver runs out of time or memory.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"muise-belle-felli-mcilraith-miller-pearce-sonenberg-planningovermultiagentepistemicstatesaclassicalplanningapproach-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.haz.ca/papers/muise-aaai-15.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'muise-belle-felli-mcilraith-miller-pearce-sonenberg-planningovermultiagentepistemicstatesaclassicalplanningapproach-2015', 'http://www.haz.ca/papers/muise-aaai-15.pdf', event);\">\n    Planning Over Multi-Agent Epistemic States: A Classical Planning Approach.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; <a class=\"bibbase author link\" href=\"https://bibbase.org/show?bib=https://dblp.org/pid/52/570.bib\">Belle, V.</a>; Felli, P.; <a class=\"bibbase author link\" href=\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\">McIlraith, S. A.</a>; <a class=\"bibbase author link\" href=\"https://uqtmiller.github.io/publications/\">Miller, T.</a>; Pearce, A. R.;  and Sonenberg, L.\n</span>\n\n  \n\n</span>\n\n  In <i>The 29th AAAI Conference on Artificial Intelligence</i>,  2015. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.haz.ca/papers/muise-aaai-15.pdf\"\n     onclick=\"log_download('muise-belle-felli-mcilraith-miller-pearce-sonenberg-planningovermultiagentepistemicstatesaclassicalplanningapproach-2015', 'http://www.haz.ca/papers/muise-aaai-15.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Planning Over Multi-Agent Epistemic States: A Classical Planning Approach [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-belle-felli-mcilraith-miller-pearce-sonenberg-planningovermultiagentepistemicstatesaclassicalplanningapproach-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>12 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-belle-felli-mcilraith-miller-pearce-sonenberg-planningovermultiagentepistemicstatesaclassicalplanningapproach-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{muise-aaai-15,\n  abstract={Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology. Our approach represents an important first step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents.},\n  title={Planning Over Multi-Agent Epistemic States: A Classical Planning Approach},\n  author={Christian Muise and Vaishak Belle and Paolo Felli and Sheila A. McIlraith and Tim Miller and Adrian R. Pearce and Liz Sonenberg},\n  booktitle={The 29th AAAI Conference on Artificial Intelligence},\n  year={2015},\n  keywords = {multi-agent planning, epistemic reasoning},\n  url={http://www.haz.ca/papers/muise-aaai-15.pdf}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology. Our approach represents an important first step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2014\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2014')\"\n      onkeypress=\"toggleGroup('group_2014')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2014</span>\n      <span class=\"bibbase_group_count\">\n        \n        (6)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"felli-miller-muise-pearce-sonenberg-artificialsocialreasoningcomputationalmechanismsforreasoningaboutothers-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://agentlab.cis.unimelb.edu.au/papers/felli-icsr-2014.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'felli-miller-muise-pearce-sonenberg-artificialsocialreasoningcomputationalmechanismsforreasoningaboutothers-2014', 'http://agentlab.cis.unimelb.edu.au/papers/felli-icsr-2014.pdf', event);\">\n    Artificial social reasoning: computational mechanisms for reasoning about others.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Felli, P.; <a class=\"bibbase author link\" href=\"https://uqtmiller.github.io/publications/\">Miller, T.</a>; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Pearce, A. R.;  and Sonenberg, L.\n</span>\n\n  \n\n</span>\n\n  In <i>The International Conference on Social Robotics</i>,  2014. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://agentlab.cis.unimelb.edu.au/papers/felli-icsr-2014.pdf\"\n     onclick=\"log_download('felli-miller-muise-pearce-sonenberg-artificialsocialreasoningcomputationalmechanismsforreasoningaboutothers-2014', 'http://agentlab.cis.unimelb.edu.au/papers/felli-icsr-2014.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Artificial social reasoning: computational mechanisms for reasoning about others [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/felli-miller-muise-pearce-sonenberg-artificialsocialreasoningcomputationalmechanismsforreasoningaboutothers-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>67 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('felli-miller-muise-pearce-sonenberg-artificialsocialreasoningcomputationalmechanismsforreasoningaboutothers-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{felli-icsr-2014,\n  abstract={With a view to supporting expressive, but tractable, collaborative interactions between humans and agents, we propose an approach for representing heterogeneous agent models, i.e., with potentially diverse mental abilities and holding stereotypical characteristics as members of a social reference group. We build a computationally grounded mechanism for progressing their beliefs about others&#x27; beliefs, supporting stereotypical as well as empathic reasoning. We comment on how this approach can be used to build finite-state games, restricting the analysis of possibly large-scale problems by focusing only on the set of plausible evolutions.},\n  title={Artificial social reasoning: computational mechanisms for reasoning about others},\n  author={Felli, Paolo and Miller, Tim and Muise, Christian and Pearce, Adrian R. and Sonenberg, Liz},\n  booktitle={The International Conference on Social Robotics},\n  keywords = {human-agent collaboration},\n  year={2014},\n  url={http://agentlab.cis.unimelb.edu.au/papers/felli-icsr-2014.pdf}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  With a view to supporting expressive, but tractable, collaborative interactions between humans and agents, we propose an approach for representing heterogeneous agent models, i.e., with potentially diverse mental abilities and holding stereotypical characteristics as members of a social reference group. We build a computationally grounded mechanism for progressing their beliefs about others' beliefs, supporting stereotypical as well as empathic reasoning. We comment on how this approach can be used to build finite-state games, restricting the analysis of possibly large-scale problems by focusing only on the set of plausible evolutions.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"miller-pearce-sonenberg-dignum-felli-muise-foundationsofhumanagentcollaborationsituationrelevantinformationsharing-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://agentlab.cis.unimelb.edu.au/papers/miller-aaai-symposium-2014.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'miller-pearce-sonenberg-dignum-felli-muise-foundationsofhumanagentcollaborationsituationrelevantinformationsharing-2014', 'http://agentlab.cis.unimelb.edu.au/papers/miller-aaai-symposium-2014.pdf', event);\">\n    Foundations of Human-Agent Collaboration: Situation-Relevant Information Sharing.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Miller, T.; Pearce, A. R.; Sonenberg, L.; Dignum, F.; Felli, P.;  and <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>The AAAI 2014 Fall Symposium on AI for Human-Robot Interaction</i>,  2014. \n  <span class=\"note\">Extended Abstract</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://agentlab.cis.unimelb.edu.au/papers/miller-aaai-symposium-2014.pdf\"\n     onclick=\"log_download('miller-pearce-sonenberg-dignum-felli-muise-foundationsofhumanagentcollaborationsituationrelevantinformationsharing-2014', 'http://agentlab.cis.unimelb.edu.au/papers/miller-aaai-symposium-2014.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Foundations of Human-Agent Collaboration: Situation-Relevant Information Sharing [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/miller-pearce-sonenberg-dignum-felli-muise-foundationsofhumanagentcollaborationsituationrelevantinformationsharing-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>14 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('miller-pearce-sonenberg-dignum-felli-muise-foundationsofhumanagentcollaborationsituationrelevantinformationsharing-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{miller-aaai-symposium-2014,\n  title={Foundations of Human-Agent Collaboration: Situation-Relevant Information Sharing},\n  author={Miller, Tim and Pearce, Adrian R. and Sonenberg, Liz and Dignum, Frank and Felli, Paolo and Muise, Christian},\n  booktitle={The AAAI 2014 Fall Symposium on AI for Human-Robot Interaction},\n  year={2014},\n  keywords = {human-agent collaboration},\n  url={http://agentlab.cis.unimelb.edu.au/papers/miller-aaai-symposium-2014.pdf},\n  note = {Extended Abstract}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"lipovetzky-ramirez-muise-geffner-widthandinferencebasedplannerssiwbfsfandprobe-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://people.eng.unimelb.edu.au/nlipovetzky/papers/ipc8_siw_bfs_probe.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'lipovetzky-ramirez-muise-geffner-widthandinferencebasedplannerssiwbfsfandprobe-2014', 'http://people.eng.unimelb.edu.au/nlipovetzky/papers/ipc8_siw_bfs_probe.pdf', event);\">\n    Width and Inference Based Planners: SIW, BFS (f), and PROBE.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Lipovetzky, N.; Ramirez, M.; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>;  and Geffner, H.\n</span>\n\n  \n\n</span>\n\n  In <i>International Planning Competition Booklet (IPC-8)</i>,  2014. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://people.eng.unimelb.edu.au/nlipovetzky/papers/ipc8_siw_bfs_probe.pdf\"\n     onclick=\"log_download('lipovetzky-ramirez-muise-geffner-widthandinferencebasedplannerssiwbfsfandprobe-2014', 'http://people.eng.unimelb.edu.au/nlipovetzky/papers/ipc8_siw_bfs_probe.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Width and Inference Based Planners: SIW, BFS (f), and PROBE [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/lipovetzky-ramirez-muise-geffner-widthandinferencebasedplannerssiwbfsfandprobe-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>8 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('lipovetzky-ramirez-muise-geffner-widthandinferencebasedplannerssiwbfsfandprobe-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{lipovetzky2014width,\n  title={Width and Inference Based Planners: SIW, BFS (f), and PROBE},\n  author={Lipovetzky, Nir and Ramirez, Miquel and Muise, Christian and Geffner, Hector},\n  url={http://people.eng.unimelb.edu.au/nlipovetzky/papers/ipc8_siw_bfs_probe.pdf},\n  booktitle={International Planning Competition Booklet (IPC-8)},\n  keywords = {plan generation},\n  year={2014}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"muise-belle-mcilraith-computingcontingentplansviafullyobservablenondeterministicplanning-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.haz.ca/papers/muise-aaai-14.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'muise-belle-mcilraith-computingcontingentplansviafullyobservablenondeterministicplanning-2014', 'http://www.haz.ca/papers/muise-aaai-14.pdf', event);\">\n    Computing Contingent Plans via Fully Observable Non-Deterministic Planning.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; <a class=\"bibbase author link\" href=\"https://bibbase.org/show?bib=https://dblp.org/pid/52/570.bib\">Belle, V.</a>;  and <a class=\"bibbase author link\" href=\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\">McIlraith, S. A.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>The 28th AAAI Conference on Artificial Intelligence</i>,  2014. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.haz.ca/papers/muise-aaai-14.pdf\"\n     onclick=\"log_download('muise-belle-mcilraith-computingcontingentplansviafullyobservablenondeterministicplanning-2014', 'http://www.haz.ca/papers/muise-aaai-14.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Computing Contingent Plans via Fully Observable Non-Deterministic Planning [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-belle-mcilraith-computingcontingentplansviafullyobservablenondeterministicplanning-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>65 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-belle-mcilraith-computingcontingentplansviafullyobservablenondeterministicplanning-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{muise-aaai-14,\n  abstract={Planning with sensing actions under partial observability is\na computationally challenging problem that is fundamental\nto the realization of AI tasks in areas as diverse as robotics,\ngame playing, and diagnostic problem solving. Recent work\non generating plans for partially observable domains has advocated\nfor online planning, claiming that offline plans are\noften too large to generate. Here we push the envelope on\nthis challenging problem, proposing a technique for generating\nconditional (aka contingent) plans offline. The key to our\nplanner’s success is the reliance on state-of-the-art techniques\nfor fully observable non-deterministic (FOND) planning. In\nparticular, we use an existing compilation for converting a\nplanning problem under partial observability and sensing to\na FOND planning problem. With a modified FOND planner\nin hand, we are able to scale beyond previous techniques for\ngenerating conditional plans with solutions that are orders of\nmagnitude smaller than previously possible in some domains.},\n  title={Computing Contingent Plans via Fully Observable Non-Deterministic Planning},\n  author={Muise, Christian and Belle, Vaishak and McIlraith, Sheila A.},\n  booktitle={The 28th AAAI Conference on Artificial Intelligence},\n  year={2014},\n  keywords = {non-deterministic planning, plan generation, partial observability, sensing},\n  url={http://www.haz.ca/papers/muise-aaai-14.pdf}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Planning with sensing actions under partial observability is a computationally challenging problem that is fundamental to the realization of AI tasks in areas as diverse as robotics, game playing, and diagnostic problem solving. Recent work on generating plans for partially observable domains has advocated for online planning, claiming that offline plans are often too large to generate. Here we push the envelope on this challenging problem, proposing a technique for generating conditional (aka contingent) plans offline. The key to our planner’s success is the reliance on state-of-the-art techniques for fully observable non-deterministic (FOND) planning. In particular, we use an existing compilation for converting a planning problem under partial observability and sensing to a FOND planning problem. With a modified FOND planner in hand, we are able to scale beyond previous techniques for generating conditional plans with solutions that are orders of magnitude smaller than previously possible in some domains.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"muise-mcilraith-belle-nondeterministicplanningwithconditionaleffects-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.haz.ca/papers/muise-icaps-14.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'muise-mcilraith-belle-nondeterministicplanningwithconditionaleffects-2014', 'http://www.haz.ca/papers/muise-icaps-14.pdf', event);\">\n    Non-Deterministic Planning With Conditional Effects.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; <a class=\"bibbase author link\" href=\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\">McIlraith, S. A.</a>;  and <a class=\"bibbase author link\" href=\"https://bibbase.org/show?bib=https://dblp.org/pid/52/570.bib\">Belle, V.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>The 24th International Conference on Automated Planning and Scheduling</i>,  2014. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.haz.ca/papers/muise-icaps-14.pdf\"\n     onclick=\"log_download('muise-mcilraith-belle-nondeterministicplanningwithconditionaleffects-2014', 'http://www.haz.ca/papers/muise-icaps-14.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Non-Deterministic Planning With Conditional Effects [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-mcilraith-belle-nondeterministicplanningwithconditionaleffects-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>53 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-mcilraith-belle-nondeterministicplanningwithconditionaleffects-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{muise-icaps-14,\n  abstract={Recent advances in fully observable non-deterministic\n(FOND) planning have enabled new techniques for various\napplications, such as behaviour composition, among others.\nOne key limitation of modern FOND planners is their lack\nof native support for conditional effects. In this paper we\ndescribe an extension to PRP, the current state of the art in\nFOND planning, that supports the generation of policies for\ndomains with conditional effects and non-determinism. We\npresent core modifications to the PRP planner for this enhanced\nfunctionality without sacrificing soundness and completeness.\nAdditionally, we demonstrate the planner’s capabilities on a\nvariety of benchmarks that include actions with\nboth conditional effects and non-deterministic outcomes. The\nresulting planner opens the door to models of greater\nexpressivity, and does so without affecting PRP’s efficiency.},\n  title={Non-Deterministic Planning With Conditional Effects},\n  author={Muise, Christian and McIlraith, Sheila A. and Belle, Vaishak},\n  booktitle={The 24th International Conference on Automated Planning and Scheduling},\n  year={2014},\n  keywords = {non-deterministic planning, plan generation},\n  url={http://www.haz.ca/papers/muise-icaps-14.pdf}\n}\n\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Recent advances in fully observable non-deterministic (FOND) planning have enabled new techniques for various applications, such as behaviour composition, among others. One key limitation of modern FOND planners is their lack of native support for conditional effects. In this paper we describe an extension to PRP, the current state of the art in FOND planning, that supports the generation of policies for domains with conditional effects and non-determinism. We present core modifications to the PRP planner for this enhanced functionality without sacrificing soundness and completeness. Additionally, we demonstrate the planner’s capabilities on a variety of benchmarks that include actions with both conditional effects and non-deterministic outcomes. The resulting planner opens the door to models of greater expressivity, and does so without affecting PRP’s efficiency.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"muise-exploitingrelevancetoimproverobustnessandflexibilityinplangenerationandexecution-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.haz.ca/papers/Muise-PhD-Thesis.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'muise-exploitingrelevancetoimproverobustnessandflexibilityinplangenerationandexecution-2014', 'http://www.haz.ca/papers/Muise-PhD-Thesis.pdf', event);\">\n    Exploiting Relevance to Improve Robustness and Flexibility in Plan Generation and Execution.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  Ph.D. Thesis, University of Toronto,  2014.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.haz.ca/papers/Muise-PhD-Thesis.pdf\"\n     onclick=\"log_download('muise-exploitingrelevancetoimproverobustnessandflexibilityinplangenerationandexecution-2014', 'http://www.haz.ca/papers/Muise-PhD-Thesis.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Exploiting Relevance to Improve Robustness and Flexibility in Plan Generation and Execution [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-exploitingrelevancetoimproverobustnessandflexibilityinplangenerationandexecution-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>29 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-exploitingrelevancetoimproverobustnessandflexibilityinplangenerationandexecution-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@phdthesis{muise-phdthesis,\n  author       = {Christian Muise},\n  title        = {Exploiting Relevance to Improve Robustness and Flexibility in Plan Generation and Execution},\n  school       = {University of Toronto},\n  year         = 2014,\n  keywords     = {thesis},\n  url          = {http://www.haz.ca/papers/Muise-PhD-Thesis.pdf},\n  abstract     = {Automated plan generation and execution is an essential component of most autonomous agents. An agent’s model of the world is often incomplete or incorrect, and its environment is typically noisy. To account for potential discrepancies between the agent’s model of the world and the true state of the world, the planning techniques and representations used should enable flexible and robust agent behaviour. The agent should react swiftly when unexpected changes occur to assess the impact of the discrepancy and to accommodate as necessary. In particular, the agent should avoid unnecessary replanning and recognize changes that are irrelevant for its plan to achieve the goal.\n\nIn this dissertation we address various aspects of the planning process including (1) how to synthesize a plan, (2) what a plan should constitute and how we should represent one, and (3) how to effectively execute a plan. We enable robust and flexible agent behaviour by exploiting the notion of relevance in each of the key planning areas. Intuitively, relevance characterizes what is important to consider as a sufficient condition for some property to hold. We apply relevance to the key areas of automated planning to achieve the following contributions: (1) increased flexibility of partial-order plans, (2) improved robustness of partial-order plan execution, (3) robust execution of temporally constrained plans, and (4) improved efficiency of plan generation with non-deterministic action effects.\n\nTo increase the flexibility of partial-order plans, we introduce an effective method for generating optimally relaxed partial-order plans. For the execution of a partial-order plan, we leverage regression to generalize an input plan, resulting in an execution monitoring framework that is far more robust than previous approaches. We incorporate the expressive power of temporal constraints and provide a means for monitoring the execution of a temporally constrained plan, building on our approach for executing a partial-order plan. Finally, we introduce a suite of techniques that leverage relevance to produce a state-of-the-art planner for domains with non-deterministic action effects. For each of these four areas, we investigate the theoretical properties surrounding our methods and empirically demonstrate their feasibility in comparison to the previous state of the art.}\n}\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Automated plan generation and execution is an essential component of most autonomous agents. An agent’s model of the world is often incomplete or incorrect, and its environment is typically noisy. To account for potential discrepancies between the agent’s model of the world and the true state of the world, the planning techniques and representations used should enable flexible and robust agent behaviour. The agent should react swiftly when unexpected changes occur to assess the impact of the discrepancy and to accommodate as necessary. In particular, the agent should avoid unnecessary replanning and recognize changes that are irrelevant for its plan to achieve the goal. In this dissertation we address various aspects of the planning process including (1) how to synthesize a plan, (2) what a plan should constitute and how we should represent one, and (3) how to effectively execute a plan. We enable robust and flexible agent behaviour by exploiting the notion of relevance in each of the key planning areas. Intuitively, relevance characterizes what is important to consider as a sufficient condition for some property to hold. We apply relevance to the key areas of automated planning to achieve the following contributions: (1) increased flexibility of partial-order plans, (2) improved robustness of partial-order plan execution, (3) robust execution of temporally constrained plans, and (4) improved efficiency of plan generation with non-deterministic action effects. To increase the flexibility of partial-order plans, we introduce an effective method for generating optimally relaxed partial-order plans. For the execution of a partial-order plan, we leverage regression to generalize an input plan, resulting in an execution monitoring framework that is far more robust than previous approaches. We incorporate the expressive power of temporal constraints and provide a means for monitoring the execution of a temporally constrained plan, building on our approach for executing a partial-order plan. Finally, we introduce a suite of techniques that leverage relevance to produce a state-of-the-art planner for domains with non-deterministic action effects. For each of these four areas, we investigate the theoretical properties surrounding our methods and empirically demonstrate their feasibility in comparison to the previous state of the art.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2013\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2013')\"\n      onkeypress=\"toggleGroup('group_2013')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2013</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"klebanov-manthey-muise-satbasedanalysisandquantificationofinformationflowinprograms-2013\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://formal.iti.kit.edu/~klebanov/pubs/qest2013.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'klebanov-manthey-muise-satbasedanalysisandquantificationofinformationflowinprograms-2013', 'http://formal.iti.kit.edu/~klebanov/pubs/qest2013.pdf', event);\">\n    SAT-based Analysis and Quantification of Information Flow in Programs.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Klebanov, V.; Manthey, N.;  and <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>10th International Conference on Quantitative Evaluation of SysTems (QEST 2013)</i>, pages 177–192,  2013. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://formal.iti.kit.edu/~klebanov/pubs/qest2013.pdf\"\n     onclick=\"log_download('klebanov-manthey-muise-satbasedanalysisandquantificationofinformationflowinprograms-2013', 'http://formal.iti.kit.edu/~klebanov/pubs/qest2013.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"SAT-based Analysis and Quantification of Information Flow in Programs [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/klebanov-manthey-muise-satbasedanalysisandquantificationofinformationflowinprograms-2013\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>61 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('klebanov-manthey-muise-satbasedanalysisandquantificationofinformationflowinprograms-2013')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{klebanovsat,\n  title={{SAT-based Analysis and Quantification of Information Flow in Programs}},\n  author={Klebanov, Vladimir and Manthey, Norbert and Muise, Christian},\n  booktitle={10th International Conference on Quantitative Evaluation of SysTems (QEST 2013)},\n  year={2013},\n  pages={177--192},\n  keywords = {sat, knowledge compilation},\n  url={http://formal.iti.kit.edu/~klebanov/pubs/qest2013.pdf}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"muise-beck-mcilraith-flexibleexecutionofpartialorderplanswithtemporalconstraints-2013\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://ijcai.org/papers13/Papers/IJCAI13-343.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'muise-beck-mcilraith-flexibleexecutionofpartialorderplanswithtemporalconstraints-2013', 'http://ijcai.org/papers13/Papers/IJCAI13-343.pdf', event);\">\n    Flexible Execution of Partial Order Plans With Temporal Constraints.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Beck, J. C.;  and <a class=\"bibbase author link\" href=\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\">McIlraith, S. A.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>International Joint Conference On Artificial Intelligence</i>, pages 2328–2335,  2013. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://ijcai.org/papers13/Papers/IJCAI13-343.pdf\"\n     onclick=\"log_download('muise-beck-mcilraith-flexibleexecutionofpartialorderplanswithtemporalconstraints-2013', 'http://ijcai.org/papers13/Papers/IJCAI13-343.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Flexible Execution of Partial Order Plans With Temporal Constraints [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-beck-mcilraith-flexibleexecutionofpartialorderplanswithtemporalconstraints-2013\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>14 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-beck-mcilraith-flexibleexecutionofpartialorderplanswithtemporalconstraints-2013')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{muise-ijcai-13,\n  title={{Flexible Execution of Partial Order Plans With Temporal Constraints}},\n  author={Muise, Christian and Beck, J. Christopher and McIlraith, Sheila A.},\n  booktitle={International Joint Conference On Artificial Intelligence},\n  year={2013},\n  pages={2328--2335},\n  keywords = {plan execution,temporal},\n  url={http://ijcai.org/papers13/Papers/IJCAI13-343.pdf}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2012\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2012')\"\n      onkeypress=\"toggleGroup('group_2012')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2012</span>\n      <span class=\"bibbase_group_count\">\n        \n        (4)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"muise-mcilraith-beck-improvednondeterministicplanningbyexploitingstaterelevance-2012\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.haz.ca/papers/muise-icaps2012-fond.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'muise-mcilraith-beck-improvednondeterministicplanningbyexploitingstaterelevance-2012', 'http://www.haz.ca/papers/muise-icaps2012-fond.pdf', event);\">\n    Improved Non-deterministic Planning by Exploiting State Relevance.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; <a class=\"bibbase author link\" href=\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\">McIlraith, S. A.</a>;  and Beck, J. C.\n</span>\n\n  \n\n</span>\n\n  In <i>The 22nd International Conference on Automated Planning and Scheduling</i>,  2012. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.haz.ca/papers/muise-icaps2012-fond.pdf\"\n     onclick=\"log_download('muise-mcilraith-beck-improvednondeterministicplanningbyexploitingstaterelevance-2012', 'http://www.haz.ca/papers/muise-icaps2012-fond.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Improved Non-deterministic Planning by Exploiting State Relevance [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-mcilraith-beck-improvednondeterministicplanningbyexploitingstaterelevance-2012\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>99 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-mcilraith-beck-improvednondeterministicplanningbyexploitingstaterelevance-2012')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{muise-icaps12-long,\nabstract = {We address the problem of computing a policy for fully ob- servable non-deterministic (FOND) planning problems. By focusing on the relevant aspects of the state of the world, we introduce a series of improvements to the previous state of the art and extend the applicability of our planner, PRP, to work in an online setting. The use of state relevance allows our policy to be exponentiallymore succinct in representing a solution to a FOND problem for some domains. Through the introduction of newtechniques for avoiding deadends and de- termining sufficient validity conditions, PRP has the potential to compute a policy up to several orders of magnitude faster than previous approaches. We also find dramatic improve- ments over the state of the art in online replanning when we treat suitable probabilistic domains as FOND domains.},\nauthor = {Muise, Christian and McIlraith, Sheila A. and Beck, J. Christopher},\nbooktitle = {The 22nd International Conference on Automated Planning and Scheduling},\nseries = {The 22nd International Conference on Automated Planning and Scheduling},\ntitle = {{Improved Non-deterministic Planning by Exploiting State Relevance}},\nkeywords = {plan generation,non-deterministic planning},\nyear = {2012},\nurl = {http://www.haz.ca/papers/muise-icaps2012-fond.pdf}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  We address the problem of computing a policy for fully ob- servable non-deterministic (FOND) planning problems. By focusing on the relevant aspects of the state of the world, we introduce a series of improvements to the previous state of the art and extend the applicability of our planner, PRP, to work in an online setting. The use of state relevance allows our policy to be exponentiallymore succinct in representing a solution to a FOND problem for some domains. Through the introduction of newtechniques for avoiding deadends and de- termining sufficient validity conditions, PRP has the potential to compute a policy up to several orders of magnitude faster than previous approaches. We also find dramatic improve- ments over the state of the art in online replanning when we treat suitable probabilistic domains as FOND domains.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"muise-mcilratih-beck-optimallyrelaxingpartialorderplanswithmaxsat-2012\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://tidel.mie.utoronto.ca/pubs/muise_MaxSATICAPS2012.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'muise-mcilratih-beck-optimallyrelaxingpartialorderplanswithmaxsat-2012', 'http://tidel.mie.utoronto.ca/pubs/muise_MaxSATICAPS2012.pdf', event);\">\n    Optimally Relaxing Partial-order Plans With MaxSAT.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; McIlratih, S. A.;  and Beck, J. C.\n</span>\n\n  \n\n</span>\n\n  In <i>22nd International Conference on Automated Planning and Scheduling</i>,  2012. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://tidel.mie.utoronto.ca/pubs/muise_MaxSATICAPS2012.pdf\"\n     onclick=\"log_download('muise-mcilratih-beck-optimallyrelaxingpartialorderplanswithmaxsat-2012', 'http://tidel.mie.utoronto.ca/pubs/muise_MaxSATICAPS2012.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Optimally Relaxing Partial-order Plans With MaxSAT [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-mcilratih-beck-optimallyrelaxingpartialorderplanswithmaxsat-2012\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>7 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-mcilratih-beck-optimallyrelaxingpartialorderplanswithmaxsat-2012')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{muise2012optimally,\n  title={{Optimally Relaxing Partial-order Plans With MaxSAT}},\n  author={Muise, Christian and McIlratih, Sheila A. and Beck, J. Christopher},\n  booktitle={22nd International Conference on Automated Planning and Scheduling},\n  url={http://tidel.mie.utoronto.ca/pubs/muise_MaxSATICAPS2012.pdf},\n  keywords = {sat,plan optimization},\n  year={2012}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"muise-mcilraith-beck-hsu-dsharpfastddnnfcompilationwithsharpsat-2012\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://tidel.mie.utoronto.ca/pubs/MuiseDSHARP_AI2012.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'muise-mcilraith-beck-hsu-dsharpfastddnnfcompilationwithsharpsat-2012', 'http://tidel.mie.utoronto.ca/pubs/MuiseDSHARP_AI2012.pdf', event);\">\n    DSHARP: Fast d-DNNF Compilation with sharpSAT.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; <a class=\"bibbase author link\" href=\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\">McIlraith, S. A.</a>; Beck, J. C.;  and Hsu, E.\n</span>\n\n  \n\n</span>\n\n  In <i>Canadian Conference on Artificial Intelligence</i>,  2012. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://tidel.mie.utoronto.ca/pubs/MuiseDSHARP_AI2012.pdf\"\n     onclick=\"log_download('muise-mcilraith-beck-hsu-dsharpfastddnnfcompilationwithsharpsat-2012', 'http://tidel.mie.utoronto.ca/pubs/MuiseDSHARP_AI2012.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"DSHARP: Fast d-DNNF Compilation with sharpSAT [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-mcilraith-beck-hsu-dsharpfastddnnfcompilationwithsharpsat-2012\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>7 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-mcilraith-beck-hsu-dsharpfastddnnfcompilationwithsharpsat-2012')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{Muise2012,\nauthor = {Muise, Christian and McIlraith, Sheila A. and Beck, J. Christopher and Hsu, Eric},\nbooktitle = {Canadian Conference on Artificial Intelligence},\ntitle = {{DSHARP: Fast d-DNNF Compilation with sharpSAT}},\nkeywords = {sat, knowledge compilation},\nurl = {http://tidel.mie.utoronto.ca/pubs/MuiseDSHARP_AI2012.pdf},\nyear = {2012}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"degiacomo-lesprance-muise-onsupervisingagentsinsituationdeterminedcongolog-2012\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.cse.yorku.ca/~lesperan/papers/AAMAS12.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'degiacomo-lesprance-muise-onsupervisingagentsinsituationdeterminedcongolog-2012', 'http://www.cse.yorku.ca/~lesperan/papers/AAMAS12.pdf', event);\">\n    On supervising agents in situation-determined ConGolog.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  De Giacomo, G.; Lespérance, Y.;  and <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems-Volume 2</i>, pages 1031–1038,  2012. International Foundation for Autonomous Agents and Multiagent Systems\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.cse.yorku.ca/~lesperan/papers/AAMAS12.pdf\"\n     onclick=\"log_download('degiacomo-lesprance-muise-onsupervisingagentsinsituationdeterminedcongolog-2012', 'http://www.cse.yorku.ca/~lesperan/papers/AAMAS12.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"On supervising agents in situation-determined ConGolog [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/degiacomo-lesprance-muise-onsupervisingagentsinsituationdeterminedcongolog-2012\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('degiacomo-lesprance-muise-onsupervisingagentsinsituationdeterminedcongolog-2012')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{de2012supervising,\n  title={{On supervising agents in situation-determined ConGolog}},\n  author={De Giacomo, Giuseppe and Lesp{\\&#x27;e}rance, Yves and Muise, Christian},\n  booktitle={Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems-Volume 2},\n  pages={1031--1038},\n  year={2012},\n  keywords = {sitcalc},\n  organization={International Foundation for Autonomous Agents and Multiagent Systems},\n  url={http://www.cse.yorku.ca/~lesperan/papers/AAMAS12.pdf}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2011\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2011')\"\n      onkeypress=\"toggleGroup('group_2011')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2011</span>\n      <span class=\"bibbase_group_count\">\n        \n        (4)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"muise-mcilraith-beck-monitoringtheexecutionofpartialorderplansviaregression-2011\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://ijcai.org/papers11/Papers/IJCAI11-330.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'muise-mcilraith-beck-monitoringtheexecutionofpartialorderplansviaregression-2011', 'http://ijcai.org/papers11/Papers/IJCAI11-330.pdf', event);\">\n    Monitoring the Execution of Partial-Order Plans via Regression.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; <a class=\"bibbase author link\" href=\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\">McIlraith, S. A.</a>;  and Beck, J. C.\n</span>\n\n  \n\n</span>\n\n  In <i>International Joint Conference On Artificial Intelligence</i>,  2011. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://ijcai.org/papers11/Papers/IJCAI11-330.pdf\"\n     onclick=\"log_download('muise-mcilraith-beck-monitoringtheexecutionofpartialorderplansviaregression-2011', 'http://ijcai.org/papers11/Papers/IJCAI11-330.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Monitoring the Execution of Partial-Order Plans via Regression [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-mcilraith-beck-monitoringtheexecutionofpartialorderplansviaregression-2011\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>14 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-mcilraith-beck-monitoringtheexecutionofpartialorderplansviaregression-2011')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{muise-ijcai11,\nabstract = {Partial-order plans (POPs) have the capacity to compactly represent numerous distinct plan lin- earizations and as a consequence are inherently ro- bust. We exploit this robustness to do effective ex- ecutionmonitoring. We characterize the conditions underwhich a POP remains viable as the regression of the goal through the structure of a POP.We then develop a method for POP execution monitoring via a structured policy, expressed as an ordered al- gebraic decision diagram. The policy encompasses both state evaluation and action selection, enabling an agent to seamlessly switch between POP lin- earizations to accommodate unexpected changes during execution. We demonstrate the effective- ness of our approach by comparing it empirically and analytically to a standard technique for execu- tion monitoring of sequential plans. On standard benchmark planning domains, our approach is 2 to 17 times faster and up to 2.5 times more robust than comparable monitoring of a sequential plan. On POPs that have few ordering constraints among actions, our approach is significantly more robust, with the ability to continue executing in up to an exponential number of additional states.},\nauthor = {Muise, Christian and McIlraith, Sheila A. and Beck, J. Christopher},\nbooktitle = {International Joint Conference On Artificial Intelligence},\nseries = {International Joint Conference On Artificial Intelligence},\ntitle = {{Monitoring the Execution of Partial-Order Plans via Regression}},\nyear = {2011},\nkeywords = {plan execution},\nurl = {http://ijcai.org/papers11/Papers/IJCAI11-330.pdf}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Partial-order plans (POPs) have the capacity to compactly represent numerous distinct plan lin- earizations and as a consequence are inherently ro- bust. We exploit this robustness to do effective ex- ecutionmonitoring. We characterize the conditions underwhich a POP remains viable as the regression of the goal through the structure of a POP.We then develop a method for POP execution monitoring via a structured policy, expressed as an ordered al- gebraic decision diagram. The policy encompasses both state evaluation and action selection, enabling an agent to seamlessly switch between POP lin- earizations to accommodate unexpected changes during execution. We demonstrate the effective- ness of our approach by comparing it empirically and analytically to a standard technique for execu- tion monitoring of sequential plans. On standard benchmark planning domains, our approach is 2 to 17 times faster and up to 2.5 times more robust than comparable monitoring of a sequential plan. On POPs that have few ordering constraints among actions, our approach is significantly more robust, with the ability to continue executing in up to an exponential number of additional states.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"muise-mcilratih-beck-optimizationofpartialorderplansviamaxsat-2011\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.haz.ca/papers/coplas-muise-11.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'muise-mcilratih-beck-optimizationofpartialorderplansviamaxsat-2011', 'http://www.haz.ca/papers/coplas-muise-11.pdf', event);\">\n    Optimization of Partial-Order Plans via MaxSAT.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; McIlratih, S. A.;  and Beck, J. C.\n</span>\n\n  \n\n</span>\n\n  In <i>Workshop on Constraint Satisfaction Techniques for Planning and Scheduling Problems (COPLAS'11)</i>,  2011. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.haz.ca/papers/coplas-muise-11.pdf\"\n     onclick=\"log_download('muise-mcilratih-beck-optimizationofpartialorderplansviamaxsat-2011', 'http://www.haz.ca/papers/coplas-muise-11.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Optimization of Partial-Order Plans via MaxSAT [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-mcilratih-beck-optimizationofpartialorderplansviamaxsat-2011\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-mcilratih-beck-optimizationofpartialorderplansviamaxsat-2011')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{muise2011optimally,\n  title={{Optimization of Partial-Order Plans via MaxSAT}},\n  author={Muise, Christian and McIlratih, Sheila A. and Beck, J. Christopher},\n  booktitle={Workshop on Constraint Satisfaction Techniques for Planning and Scheduling Problems (COPLAS&#x27;11)},\n  year={2011},\n  keywords = {sat,plan optimization},\n  url={http://www.haz.ca/papers/coplas-muise-11.pdf}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"degiacomo-lesperance-muise-agentsupervisioninsituationdeterminedcongolog-2011\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://ijcai-11.iiia.csic.es/files/proceedings/W4- NRAC11-Proceedings.pdf\\#page=27\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'degiacomo-lesperance-muise-agentsupervisioninsituationdeterminedcongolog-2011', 'http://ijcai-11.iiia.csic.es/files/proceedings/W4- NRAC11-Proceedings.pdf\\#page=27', event);\">\n    Agent Supervision in Situation-Determined ConGolog.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  De Giacomo, G.; Lesperance, Y.;  and <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>Nonmonotonic Reasoning, Action and Change</i>,  2011. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://ijcai-11.iiia.csic.es/files/proceedings/W4- NRAC11-Proceedings.pdf\\#page=27\"\n     onclick=\"log_download('degiacomo-lesperance-muise-agentsupervisioninsituationdeterminedcongolog-2011', 'http://ijcai-11.iiia.csic.es/files/proceedings/W4- NRAC11-Proceedings.pdf\\#page=27', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Agent Supervision in Situation-Determined ConGolog [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/degiacomo-lesperance-muise-agentsupervisioninsituationdeterminedcongolog-2011\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('degiacomo-lesperance-muise-agentsupervisioninsituationdeterminedcongolog-2011')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{DeGiacomo2011,\nabstract = {We investigate agent supervision, a form of customization, which constrains the actions of an agent so as to enforce certain desired behavioral specifications. This is done in a setting based on the Situation Calculus and a variant of the ConGolog programming language which allows for nondeterminism, but requires the remainder of a program after the execution of an action to be determined by the resulting situation. Such programs can be fully characterized by the set of action sequences that they generate. The main results are a characterization of the maximally permissive supervisor that minimally constrains the agent so as to enforce the desired behavioral constraints when some agent actions are uncontrollable, and a sound and complete technique to execute the agent as constrained by such a supervisor.},\nauthor = {{De Giacomo}, Giuseppe and Lesperance, Yves and Muise, Christian},\nbooktitle = {Nonmonotonic Reasoning, Action and Change},\ntitle = {{Agent Supervision in Situation-Determined ConGolog}},\nurl = {http://ijcai-11.iiia.csic.es/files/proceedings/W4- NRAC11-Proceedings.pdf\\#page=27},\nkeywords = {sitcalc},\nyear = {2011}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  We investigate agent supervision, a form of customization, which constrains the actions of an agent so as to enforce certain desired behavioral specifications. This is done in a setting based on the Situation Calculus and a variant of the ConGolog programming language which allows for nondeterminism, but requires the remainder of a program after the execution of an action to be determined by the resulting situation. Such programs can be fully characterized by the set of action sequences that they generate. The main results are a characterization of the maximally permissive supervisor that minimally constrains the agent so as to enforce the desired behavioral constraints when some agent actions are uncontrollable, and a sound and complete technique to execute the agent as constrained by such a supervisor.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"muise-plandispatchabilityasurvey-2011\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.haz.ca/papers/muise-depth.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'muise-plandispatchabilityasurvey-2011', 'http://www.haz.ca/papers/muise-depth.pdf', event);\">\n    Plan Dispatchability: A Survey.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  Technical Report University of Toronto, Depth Examination Report,  2011.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.haz.ca/papers/muise-depth.pdf\"\n     onclick=\"log_download('muise-plandispatchabilityasurvey-2011', 'http://www.haz.ca/papers/muise-depth.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Plan Dispatchability: A Survey [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"http://www.haz.ca/papers/muise-depth_slides.pdf\"\n     onclick=\"log_download('muise-plandispatchabilityasurvey-2011', 'http://www.haz.ca/papers/muise-depth_slides.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Plan Dispatchability: A Survey [pdf]\"\n       title=\" slides\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> slides</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-plandispatchabilityasurvey-2011\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>3 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-plandispatchabilityasurvey-2011')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@techreport{muise-depth,\n  title={Plan Dispatchability: A Survey},\n  author={Christian Muise},\n  year={2011},\n  institution={University of Toronto, Depth Examination Report},\n  keywords={temporal},\n  url={http://www.haz.ca/papers/muise-depth.pdf},\n  url_slides={http://www.haz.ca/papers/muise-depth_slides.pdf}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2010\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2010')\"\n      onkeypress=\"toggleGroup('group_2010')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2010</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"muise-mcilraith-beck-fastddnnfcompilationwithsharpsat-2010\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.haz.ca/papers/wara-muise-10.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'muise-mcilraith-beck-fastddnnfcompilationwithsharpsat-2010', 'http://www.haz.ca/papers/wara-muise-10.pdf', event);\">\n    Fast d-DNNF Compilation with sharpSAT.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; McIlraith, S. A.;  and Beck, J. C.\n</span>\n\n  \n\n</span>\n\n  In <i>Workshop on Abstraction, Reformulation, and Approximation (AAAI-10)</i>, Atlanta, GA, USA,  2010. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.haz.ca/papers/wara-muise-10.pdf\"\n     onclick=\"log_download('muise-mcilraith-beck-fastddnnfcompilationwithsharpsat-2010', 'http://www.haz.ca/papers/wara-muise-10.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Fast d-DNNF Compilation with sharpSAT [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-mcilraith-beck-fastddnnfcompilationwithsharpsat-2010\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-mcilraith-beck-fastddnnfcompilationwithsharpsat-2010')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{mui-etal-wara10,\naddress = {Atlanta, GA, USA},\nauthor = {Muise, Christian and McIlraith, Sheila A. and Beck, J. Christopher},\nbooktitle = {Workshop on Abstraction, Reformulation, and Approximation (AAAI-10)},\ntitle = {{Fast d-DNNF Compilation with sharpSAT}},\nurl = {http://www.haz.ca/papers/wara-muise-10.pdf},\nkeywords = {sat, knowledge compilation},\nyear = {2010}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2009\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2009')\"\n      onkeypress=\"toggleGroup('group_2009')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2009</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"muise-mcilraith-baier-reimer-exploitingngramanalysistopredictoperatorsequences-2009\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.haz.ca/papers/mui-etal-icaps09.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'muise-mcilraith-baier-reimer-exploitingngramanalysistopredictoperatorsequences-2009', 'http://www.haz.ca/papers/mui-etal-icaps09.pdf', event);\">\n    Exploiting N-gram Analysis to Predict Operator Sequences.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; <a class=\"bibbase author link\" href=\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\">McIlraith, S. A.</a>; <a class=\"bibbase author link\" href=\"http://www.cs.toronto.edu/~bgmomb/research.html\">Baier, J. A.</a>;  and Reimer, M.\n</span>\n\n  \n\n</span>\n\n  In <i>19th International Conference on Automated Planning and Scheduling</i>, Thessaloniki, Greece,  2009. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.haz.ca/papers/mui-etal-icaps09.pdf\"\n     onclick=\"log_download('muise-mcilraith-baier-reimer-exploitingngramanalysistopredictoperatorsequences-2009', 'http://www.haz.ca/papers/mui-etal-icaps09.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Exploiting N-gram Analysis to Predict Operator Sequences [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-mcilraith-baier-reimer-exploitingngramanalysistopredictoperatorsequences-2009\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>14 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-mcilraith-baier-reimer-exploitingngramanalysistopredictoperatorsequences-2009')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{Muise2009,\naddress = {Thessaloniki, Greece},\nauthor = {Muise, Christian and McIlraith, Sheila A. and Baier, Jorge A. and Reimer, Michael},\nbooktitle = {19th International Conference on Automated Planning and Scheduling},\ntitle = {{Exploiting N-gram Analysis to Predict Operator Sequences}},\nurl = {http://www.haz.ca/papers/mui-etal-icaps09.pdf},\nyear = {2009},\nkeywords = {learning},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"muise-exploitingmodernsatsolvingtechniquestogenerateimplicants-2009\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.haz.ca/papers/Muise-Masters-Thesis.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'muise-exploitingmodernsatsolvingtechniquestogenerateimplicants-2009', 'http://www.haz.ca/papers/Muise-Masters-Thesis.pdf', event);\">\n    Exploiting Modern #SAT-Solving Techniques to Generate Implicants.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>\n</span>\n\n  \n\n</span>\n\n  Master's thesis, University of Toronto,  2009.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.haz.ca/papers/Muise-Masters-Thesis.pdf\"\n     onclick=\"log_download('muise-exploitingmodernsatsolvingtechniquestogenerateimplicants-2009', 'http://www.haz.ca/papers/Muise-Masters-Thesis.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Exploiting Modern #SAT-Solving Techniques to Generate Implicants [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/muise-exploitingmodernsatsolvingtechniquestogenerateimplicants-2009\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>3 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('muise-exploitingmodernsatsolvingtechniquestogenerateimplicants-2009')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@mastersthesis{muise-mastersthesis,\n  author       = {Christian Muise},\n  title        = {Exploiting Modern \\#SAT-Solving Techniques to Generate Implicants},\n  school       = {University of Toronto},\n  year         = 2009,\n  keywords     = {thesis},\n  url          = {http://www.haz.ca/papers/Muise-Masters-Thesis.pdf},\n  abstract     = {An implicant of a propositional boolean formula is a conjunction of propositions that entails a the formula. Implicants play an important role for many tasks in AI such as knowledge compilation, circuit minimization, and diagnosis. There are many classes of implicants that are of interest including prime, irredundant, and orthogonal implicants. In general, implicants are hard to compute because the size of both the intermediate and/or final results can be exponentially large. In this work we focus on how advances in modern #SAT solvers can be exploited to effectively compute a complete set of orthogonal implicants. We develop an orthogonal implicant compiler and demonstrate its potential through experimental evaluation. Preliminary results indicate that this approach is effective at generating the orthogonal implicants of a propositional theory represented in Conjunctive Normal Form.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  An implicant of a propositional boolean formula is a conjunction of propositions that entails a the formula. Implicants play an important role for many tasks in AI such as knowledge compilation, circuit minimization, and diagnosis. There are many classes of implicants that are of interest including prime, irredundant, and orthogonal implicants. In general, implicants are hard to compute because the size of both the intermediate and/or final results can be exponentially large. In this work we focus on how advances in modern #SAT solvers can be exploited to effectively compute a complete set of orthogonal implicants. We develop an orthogonal implicant compiler and demonstrate its potential through experimental evaluation. Preliminary results indicate that this approach is effective at generating the orthogonal implicants of a propositional theory represented in Conjunctive Normal Form.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2008\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2008')\"\n      onkeypress=\"toggleGroup('group_2008')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2008</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"hsu-muise-beck-mcilraith-probabilisticallyestimatingbackbonesandvariablebiasexperimentaloverview-2008\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.haz.ca/papers/hsu-cp2008.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'hsu-muise-beck-mcilraith-probabilisticallyestimatingbackbonesandvariablebiasexperimentaloverview-2008', 'http://www.haz.ca/papers/hsu-cp2008.pdf', event);\">\n    Probabilistically Estimating Backbones and Variable Bias: Experimental Overview.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Hsu, E. I; <a class=\"bibbase author link\" href=\"https://www.haz.ca/academic-publications.html\">Muise, C.</a>; Beck, J. C.;  and <a class=\"bibbase author link\" href=\"https://www.cs.toronto.edu/~toryn/test/publications/ltl_planning.html\">McIlraith, S. A.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>Principles and Practice of Constraint Programming, 14th International Conference</i>, pages 613–617, Sydney, Australia,  2008. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.haz.ca/papers/hsu-cp2008.pdf\"\n     onclick=\"log_download('hsu-muise-beck-mcilraith-probabilisticallyestimatingbackbonesandvariablebiasexperimentaloverview-2008', 'http://www.haz.ca/papers/hsu-cp2008.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Probabilistically Estimating Backbones and Variable Bias: Experimental Overview [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/hsu-muise-beck-mcilraith-probabilisticallyestimatingbackbonesandvariablebiasexperimentaloverview-2008\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>4 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('hsu-muise-beck-mcilraith-probabilisticallyestimatingbackbonesandvariablebiasexperimentaloverview-2008')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{DBLP:conf/cp/HsuMBM08,\naddress = {Sydney, Australia},\nauthor = {Hsu, Eric I and Muise, Christian and Beck, J. Christopher and McIlraith, Sheila A.},\nbooktitle = {Principles and Practice of Constraint Programming, 14th International Conference},\npages = {613--617},\nkeywords = {sat},\ntitle = {{Probabilistically Estimating Backbones and Variable Bias: Experimental Overview}},\nurl = {http://www.haz.ca/papers/hsu-cp2008.pdf},\nyear = {2008}\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n\n\n\n  </div>\n\n\n  <!-- Modal -->\n\n  <!-- <iframe id=\"bibbase_network_frame\" -->\n  <!--         src=\"//bibbase.org/network/control\" -->\n  <!--         style=\"display: none;\"> -->\n  <!-- </iframe> -->\n\n</div>\n"}; document.write(bibbase_data.data);