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\n  \n 2023\n \n \n (9)\n \n \n
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\n \n\n \n \n \n \n \n \n Proceedings of the 6th ICAPS Workshop on Hierarchical Planning (HPlan 2023).\n \n \n \n \n\n\n \n Pascal Bercher; Daniel Höller; Julia Wichlacz; and Ron Alford.,\n editors.\n \n\n\n \n\n\n\n 2023.\n \n\n\n\n
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@Proceedings{HPlan2023proceedings,\n  editor          = {Pascal Bercher and Daniel H\\"oller and Julia Wichlacz and Ron Alford},\n  title           = {Proceedings of the 6th ICAPS Workshop on Hierarchical Planning (HPlan 2023)},\n  year            = {2023},\n  url_website     = {http://hplan2023.hierarchical-task.net},\n  url_proceedings = {https://icaps23.icaps-conference.org/papers/hplan/HPlanProceedings-2023.pdf}\n}\n\n\n
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\n \n\n \n \n \n \n \n \n A Look-Ahead Technique for Search-Based HTN Planning: Reducing the Branching Factor by Identifying Inevitable Task Refinements.\n \n \n \n \n\n\n \n Conny Olz; and Pascal Bercher.\n\n\n \n\n\n\n In Proceedings of the 16th International Symposium on Combinatorial Search (SoCS 2023), pages 65–73, 2023. \n \n\n\n\n
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@InProceedings{Olz2023TOLookAhead,\n  author    = {Conny Olz and Pascal Bercher},\n  title     = {A Look-Ahead Technique for Search-Based HTN Planning: Reducing the Branching Factor by Identifying Inevitable Task Refinements},\n  booktitle = {Proceedings of the 16th International Symposium on Combinatorial Search (SoCS 2023)},\n  year      = {2023},\n  doi       = {10.1609/socs.v16i1.27284},\n  pages     = {65--73},\n  abstract  = {In HTN planning the choice of decomposition methods used to refine compound tasks is key to finding a valid plan. Based on inferred preconditions and effects of compound tasks, we propose a look-ahead technique for search-based total-order HTN planning that can identify inevitable refinement choices and in some cases dead-ends. The former occurs when all but one decomposition method for some task are proven infeasible for turning a task network into a solution, whereas the latter occurs when all methods are proven infeasible. We show how it can be used for pruning, as well as to strengthen heuristics and to reduce the search branching factor. An empirical evaluation proves its potential as incorporating it improves an existing HTN planner such that it is the currently best performing one in terms of coverage and IPC score.},\n  url_paper = {https://ojs.aaai.org/index.php/SOCS/article/view/27284/27057}\n}\n\n
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\n In HTN planning the choice of decomposition methods used to refine compound tasks is key to finding a valid plan. Based on inferred preconditions and effects of compound tasks, we propose a look-ahead technique for search-based total-order HTN planning that can identify inevitable refinement choices and in some cases dead-ends. The former occurs when all but one decomposition method for some task are proven infeasible for turning a task network into a solution, whereas the latter occurs when all methods are proven infeasible. We show how it can be used for pruning, as well as to strengthen heuristics and to reduce the search branching factor. An empirical evaluation proves its potential as incorporating it improves an existing HTN planner such that it is the currently best performing one in terms of coverage and IPC score.\n
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\n \n\n \n \n \n \n \n \n Implicit Dependency Detection for HTN Plan Repair.\n \n \n \n \n\n\n \n Paul Zaidins; Mark Roberts; and Dana Nau.\n\n\n \n\n\n\n In Proceedings of the 6th ICAPS Workshop on Hierarchical Planning (HPlan 2023), pages 10–18, 2023. \n \n\n\n\n
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@InProceedings{Zaidins2023HTNPlanRepair,\n  author    = {Paul Zaidins and Mark Roberts and Dana Nau},\n  title     = {Implicit Dependency Detection for HTN Plan Repair},\n  booktitle = {Proceedings of the 6th ICAPS Workshop on Hierarchical Planning (HPlan 2023)},\n  year      = {2023},\n  pages     = {10--18},\n  abstract  = {Two recent approaches to HTN replanning, IPyHOP and SHOPFIXER, replan by adapting the previously planned solution when an action fails. IPyHOP replans the entire solution tree after the failure, while SHOPFIXER uses pre-calculated dependency graphs to replace portions of the tree; neither uses forward simulation of the plan to predict where future failures might occur. This paper describes IPyHOPPER, which improves IPyHOP by retaining more of the information provided by the hierarchy and using forward simulation to repair minimal subtrees that contain future failures. Our experimental comparisons show that in domains where errors are not rare, IPyHOPPER is both faster and uses fewer iterations to repair than IPyHOP's repair mechanism. IPyHOPPER's repair speedups are similar to those of SHOPFIXER when given a probabilistic error model with nontrivial error rates.},\n  url_paper = {https://icaps23.icaps-conference.org/papers/hplan/HPlan2023_paper_2.pdf}\n}\n\n
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\n Two recent approaches to HTN replanning, IPyHOP and SHOPFIXER, replan by adapting the previously planned solution when an action fails. IPyHOP replans the entire solution tree after the failure, while SHOPFIXER uses pre-calculated dependency graphs to replace portions of the tree; neither uses forward simulation of the plan to predict where future failures might occur. This paper describes IPyHOPPER, which improves IPyHOP by retaining more of the information provided by the hierarchy and using forward simulation to repair minimal subtrees that contain future failures. Our experimental comparisons show that in domains where errors are not rare, IPyHOPPER is both faster and uses fewer iterations to repair than IPyHOP's repair mechanism. IPyHOPPER's repair speedups are similar to those of SHOPFIXER when given a probabilistic error model with nontrivial error rates.\n
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\n \n\n \n \n \n \n \n \n On the Computational Complexity of Plan Verification, (Bounded) Plan-Optimality Verification, and Bounded Plan Existence.\n \n \n \n \n\n\n \n Songtuan Lin; Conny Olz; Malte Helmert; and Pascal Bercher.\n\n\n \n\n\n\n In Proceedings of the 6th ICAPS Workshop on Hierarchical Planning (HPlan 2023), pages 35–43, 2023. \n \n\n\n\n
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@InProceedings{Lin2023VerificationComplexity,\n  author    = {Songtuan Lin and Conny Olz and Malte Helmert and Pascal Bercher},\n  title     = {On the Computational Complexity of Plan Verification, (Bounded) Plan-Optimality Verification, and Bounded Plan Existence},\n  booktitle = {Proceedings of the 6th ICAPS Workshop on Hierarchical Planning (HPlan 2023)},\n  year      = {2023},\n  pages     = {35--43},\n  abstract  = {In this paper we study the computational complexity of several reasoning tasks centered at the bounded plan existence problem. We do this for standard classical planning and hierarchical task network (HTN) planning and each for the grounded and the lifted representation. Whereas bounded plan existence complexity is known for classical planning, it has not been studied yet for HTN planning. For plan verification, results were available for both formalisms except the lifted representation of HTN planning. We will thus present the lower bound and the upper bound of the complexity of plan verification in lifted HTN planning and provide novel insights into its grounded counterpart, in which we show that verification is not just NP-complete in the general case, but already for a severely restricted special case. Finally, we show the computational complexity concerning the optimality of a given plan, i.e., answering the question whether such a plan is optimal, and discuss its connection to the bounded plan existence problem.},\n  url_paper = {https://icaps23.icaps-conference.org/papers/hplan/HPlan2023_paper_3.pdf}\n}\n\n
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\n In this paper we study the computational complexity of several reasoning tasks centered at the bounded plan existence problem. We do this for standard classical planning and hierarchical task network (HTN) planning and each for the grounded and the lifted representation. Whereas bounded plan existence complexity is known for classical planning, it has not been studied yet for HTN planning. For plan verification, results were available for both formalisms except the lifted representation of HTN planning. We will thus present the lower bound and the upper bound of the complexity of plan verification in lifted HTN planning and provide novel insights into its grounded counterpart, in which we show that verification is not just NP-complete in the general case, but already for a severely restricted special case. Finally, we show the computational complexity concerning the optimality of a given plan, i.e., answering the question whether such a plan is optimal, and discuss its connection to the bounded plan existence problem.\n
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\n \n\n \n \n \n \n \n \n HDDL 2.1: Towards Defining a Formalism and a Semantics for Temporal HTN Planning.\n \n \n \n \n\n\n \n Damien Pellier; Alexandre Albore; Humbert Fiorino; and Rafael Bailon-Ruiz.\n\n\n \n\n\n\n In Proceedings of the 6th ICAPS Workshop on Hierarchical Planning (HPlan 2023), pages 49–53, 2023. \n This is a challenge paper.\n\n\n\n
\n\n\n\n \n \n \"HDDL paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 8 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{Pellier2023HDDL2.1,\n  author    = {Damien Pellier and Alexandre Albore and Humbert Fiorino and Rafael Bailon-Ruiz},\n  title     = {HDDL 2.1: Towards Defining a Formalism and a Semantics for Temporal HTN Planning},\n  booktitle = {Proceedings of the 6th ICAPS Workshop on Hierarchical Planning (HPlan 2023)},\n  year      = {2023},\n  pages     = {49--53},\n  note      = {This is a challenge paper.},\n  abstract  = {Real world applications as in industry and robotics need modelling rich and diverse automated planning problems. Their resolution usually requires coordinated and concurrent action execution. In several cases, these problems are naturally decomposed in a hierarchical way and expressed by a Hierarchical Task Network (HTN) formalism. HDDL, a hierarchical extension of the Planning Domain Definition Language (PDDL), unlike PDDL 2.1 does not allow to represent planning problems with numerical and temporal constraints, which are essential for real world applications. We propose to fill the gap between HDDL and these operational needs and to extend HDDL by taking inspiration from PDDL 2.1 in order to express numerical and temporal expressions. This paper opens discussions on the semantics and the syntax needed for a future HDDL 2.1 extension.},\n  url_paper = {https://icaps23.icaps-conference.org/papers/hplan/HPlan2023_paper_4.pdf}\n}\n\n
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\n Real world applications as in industry and robotics need modelling rich and diverse automated planning problems. Their resolution usually requires coordinated and concurrent action execution. In several cases, these problems are naturally decomposed in a hierarchical way and expressed by a Hierarchical Task Network (HTN) formalism. HDDL, a hierarchical extension of the Planning Domain Definition Language (PDDL), unlike PDDL 2.1 does not allow to represent planning problems with numerical and temporal constraints, which are essential for real world applications. We propose to fill the gap between HDDL and these operational needs and to extend HDDL by taking inspiration from PDDL 2.1 in order to express numerical and temporal expressions. This paper opens discussions on the semantics and the syntax needed for a future HDDL 2.1 extension.\n
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\n \n\n \n \n \n \n \n \n On Guiding Search in HTN Temporal Planning with non Temporal Heuristics.\n \n \n \n \n\n\n \n Nicolas Cavrel; Damien Pellier; and Humbert Fiorino.\n\n\n \n\n\n\n In Proceedings of the 6th ICAPS Workshop on Hierarchical Planning (HPlan 2023), pages 28–34, 2023. \n \n\n\n\n
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@InProceedings{Cavrel2023TemporalHTNPlanning,\n  author    = {Nicolas Cavrel and Damien Pellier and Humbert Fiorino},\n  title     = {On Guiding Search in HTN Temporal Planning with non Temporal Heuristics},\n  booktitle = {Proceedings of the 6th ICAPS Workshop on Hierarchical Planning (HPlan 2023)},\n  year      = {2023},\n  pages     = {28--34},\n  abstract  = {The Hierarchical Task Network (HTN) formalism is used to express a wide variety of planning problems as task decompositions, and many techniques have been proposed to solve them. However, few works have been done on temporal HTN. This is partly due to the lack of a formal and consensual definition of what a temporal hierarchical planning problem is as well as the difficulty to develop heuristics in this context. In response to these inconveniences, we propose in this paper a new general POCL (Partial Order Causal Link) approach to represent and solve a temporal HTN problem by using existing heuristics developed to solve non temporal problems. We show experimentally that this approach is performant and can outperform the existing ones.},\n  url_paper = {https://icaps23.icaps-conference.org/papers/hplan/HPlan2023_paper_6.pdf}\n}\n\n
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\n The Hierarchical Task Network (HTN) formalism is used to express a wide variety of planning problems as task decompositions, and many techniques have been proposed to solve them. However, few works have been done on temporal HTN. This is partly due to the lack of a formal and consensual definition of what a temporal hierarchical planning problem is as well as the difficulty to develop heuristics in this context. In response to these inconveniences, we propose in this paper a new general POCL (Partial Order Causal Link) approach to represent and solve a temporal HTN problem by using existing heuristics developed to solve non temporal problems. We show experimentally that this approach is performant and can outperform the existing ones.\n
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\n \n\n \n \n \n \n \n \n Integrating Deep Learning Techniques into Hierarchical Task Planning for Effect and Heuristic Predictions in 2D Domains.\n \n \n \n \n\n\n \n Michael Staud.\n\n\n \n\n\n\n In Proceedings of the 6th ICAPS Workshop on Hierarchical Planning (HPlan 2023), pages 19–27, 2023. \n \n\n\n\n
\n\n\n\n \n \n \"Integrating paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{Staud2023DLandHTNsFor2D,\n  author    = {Michael Staud},\n  title     = {Integrating Deep Learning Techniques into Hierarchical Task Planning for Effect and Heuristic Predictions in 2D Domains},\n  booktitle = {Proceedings of the 6th ICAPS Workshop on Hierarchical Planning (HPlan 2023)},\n  year      = {2023},\n  pages     = {19--27},\n  abstract  = {In this paper, we present a novel approach that combines Hierarchical Task Planning (HTN) with deep learning techniques to address the challenges of scalability and efficiency in large-scale planning problems. Building upon the Hierarchical World State Planning (HWSP) algorithm, our method utilizes a multi-layered world state representation, which allows for planning at abstract levels without the need to consider lower-level details. We propose a deep learning method for predicting the effects of abstract tasks, which opens the door to enhancements in both planning performance and plan quality. Additionally, we employ the same approach to create a domain-dependent planning heuristic. Our contributions demonstrate the potential of integrating HTN planning with deep learning techniques, paving the way for future research in various application domains such as robotics, logistics, and urban planning. The proposed approach employs standard deep learning techniques, ensuring adaptability as the state of the art advances.},\n  url_paper = {https://icaps23.icaps-conference.org/papers/hplan/HPlan2023_paper_7.pdf}\n}\n\n
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\n In this paper, we present a novel approach that combines Hierarchical Task Planning (HTN) with deep learning techniques to address the challenges of scalability and efficiency in large-scale planning problems. Building upon the Hierarchical World State Planning (HWSP) algorithm, our method utilizes a multi-layered world state representation, which allows for planning at abstract levels without the need to consider lower-level details. We propose a deep learning method for predicting the effects of abstract tasks, which opens the door to enhancements in both planning performance and plan quality. Additionally, we employ the same approach to create a domain-dependent planning heuristic. Our contributions demonstrate the potential of integrating HTN planning with deep learning techniques, paving the way for future research in various application domains such as robotics, logistics, and urban planning. The proposed approach employs standard deep learning techniques, ensuring adaptability as the state of the art advances.\n
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\n \n\n \n \n \n \n \n \n Extracting Hierarchical Task Networks Parameters from Demonstrations.\n \n \n \n \n\n\n \n Philippe Hérail; and Arthur Bit-Monnot.\n\n\n \n\n\n\n In Proceedings of the 6th ICAPS Workshop on Hierarchical Planning (HPlan 2023), pages 1–9, 2023. \n \n\n\n\n
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@InProceedings{Herail2023HTNParametersExtraction,\n  author    = {Philippe H{\\'e}rail and Arthur Bit-Monnot},\n  title     = {Extracting Hierarchical Task Networks Parameters from Demonstrations},\n  booktitle = {Proceedings of the 6th ICAPS Workshop on Hierarchical Planning (HPlan 2023)},\n  year      = {2023},\n  pages     = {1--9},\n  abstract  = {Hierarchical Task Networks (HTNs) are a common formalism in automated planning. However, HTN models are mostly designed by hand by expert users. While many of the state-of-the-art approaches for learning HTN try and learn the structure and its parameterization in a single step, other focus specifically on learning the structure of the model. Many of these structure-focused approaches, however, learn models with non-parameterized actions, task and methods, which limits their generalization capabilities. In this paper, we propose a constraint satisfaction-based approach for extracting parameters for a given HTN structure using a set of demonstration traces.},\n  url_paper = {https://icaps23.icaps-conference.org/papers/hplan/HPlan2023_paper_8.pdf}\n}\n\n
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\n Hierarchical Task Networks (HTNs) are a common formalism in automated planning. However, HTN models are mostly designed by hand by expert users. While many of the state-of-the-art approaches for learning HTN try and learn the structure and its parameterization in a single step, other focus specifically on learning the structure of the model. Many of these structure-focused approaches, however, learn models with non-parameterized actions, task and methods, which limits their generalization capabilities. In this paper, we propose a constraint satisfaction-based approach for extracting parameters for a given HTN structure using a set of demonstration traces.\n
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\n \n\n \n \n \n \n \n \n Can HTN Planning Make Flying Alone Safer?.\n \n \n \n \n\n\n \n Jane Jean Kiam; and Prakash Jamakatel.\n\n\n \n\n\n\n In Proceedings of the 6th ICAPS Workshop on Hierarchical Planning (HPlan 2023), pages 44–48, 2023. \n This is a challenge paper.\n\n\n\n
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@InProceedings{Kiam2023FlyingWithHTNs,\n  author    = {Jane Jean Kiam and Prakash Jamakatel},\n  title     = {Can HTN Planning Make Flying Alone Safer?},\n  booktitle = {Proceedings of the 6th ICAPS Workshop on Hierarchical Planning (HPlan 2023)},\n  year      = {2023},\n  pages     = {44--48},\n  note      = {This is a challenge paper.},\n  abstract  = {Safety aspects in general aviation can be a limiting factor to gear toward introducing more single-pilot operations (SPOs), which are currently commonly practised by private pilots of ultralight aircraft, but are also a key to future developments in urban air mobility. The risks of SPOs are mainly due to the lack of redundancy, especially in case of emergeny; the development of reliable onboard companion technology is therefore deemed beneficial. This paper investigates how Hierarchical Task Networks (HTN), and more specifically the Hierarchical Domain Definition Language (HDDL), can be used to encode private pilots' maneuvers. Additionally, challenges are underlined on onboard companion technologies for SPOs, alongside with some features to be derived from hierarchical planning techniques to overcome these challenges.},\n  url_paper = {https://icaps23.icaps-conference.org/papers/hplan/HPlan2023_paper_9.pdf}\n}\n\n\n
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\n Safety aspects in general aviation can be a limiting factor to gear toward introducing more single-pilot operations (SPOs), which are currently commonly practised by private pilots of ultralight aircraft, but are also a key to future developments in urban air mobility. The risks of SPOs are mainly due to the lack of redundancy, especially in case of emergeny; the development of reliable onboard companion technology is therefore deemed beneficial. This paper investigates how Hierarchical Task Networks (HTN), and more specifically the Hierarchical Domain Definition Language (HDDL), can be used to encode private pilots' maneuvers. Additionally, challenges are underlined on onboard companion technologies for SPOs, alongside with some features to be derived from hierarchical planning techniques to overcome these challenges.\n
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\n \n\n \n \n \n \n \n \n Proceedings of the 5th ICAPS Workshop on Hierarchical Planning (HPlan 2022).\n \n \n \n \n\n\n \n Pascal Bercher; Jane Jean Kiam; Arthur Bit-Monnot; and Ron Alford.,\n editors.\n \n\n\n \n\n\n\n 2022.\n \n\n\n\n
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@Proceedings{HPlan2022proceedings,\n  editor          = {Pascal Bercher and Jane Jean Kiam and Arthur Bit-Monnot and Ron Alford},\n  title           = {Proceedings of the 5th ICAPS Workshop on Hierarchical Planning (HPlan 2022)},\n  year            = {2022},\n  url_website     = {http://hplan2022.hierarchical-task.net},\n  url_proceedings = {https://icaps22.icaps-conference.org/workshops/HPlan/papers/HPlanProceedings-2022.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n \n An Accurate HDDL Domain Learning Algorithm from Partial and Noisy Observations.\n \n \n \n \n\n\n \n Maxence Grand; Damien Pellier; and Humbert Fiorino.\n\n\n \n\n\n\n In Proceedings of the 5th ICAPS Workshop on Hierarchical Planning (HPlan 2022), pages 1–9, 2022. \n \n\n\n\n
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@InProceedings{HPlan2022paper-01,\n  author           = {Maxence Grand and Damien Pellier and Humbert Fiorino},\n  title            = {An Accurate HDDL Domain Learning Algorithm from Partial and Noisy Observations},\n  booktitle        = {Proceedings of the 5th ICAPS Workshop on Hierarchical Planning (HPlan 2022)},\n  year             = {2022},\n  pages            = {1--9},\n  abstract         = {The Hierarchical Task Network (HTN) formalism is very expressive and used to express a wide variety of planning problems. In contrast to the classical STRIPS formalism in which only the action model needs to be specified, the HTN formalism requires to specify, in addition, the tasks of the problem and their decomposition into subtasks, called HTN methods. For this reason, hand-encoding HTN problems is considered more difficult and more error-prone by experts than classical planning problem. To tackle this problem, we propose a new approach (HierAMLSI) based on grammar induction to acquire HTN planning domain knowledge, by learning action models and HTN methods with their preconditions. Unlike other approaches, HierAMLSI is able to learn both actions and methods with noisy and partial inputs observation with a high level or accuracy.},\n  url_paper        = {https://icaps22.icaps-conference.org/workshops/HPlan/papers/paper-01.pdf},\n  url_presentation = {https://youtu.be/Znp95XfIipk}\n}\n\n
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\n The Hierarchical Task Network (HTN) formalism is very expressive and used to express a wide variety of planning problems. In contrast to the classical STRIPS formalism in which only the action model needs to be specified, the HTN formalism requires to specify, in addition, the tasks of the problem and their decomposition into subtasks, called HTN methods. For this reason, hand-encoding HTN problems is considered more difficult and more error-prone by experts than classical planning problem. To tackle this problem, we propose a new approach (HierAMLSI) based on grammar induction to acquire HTN planning domain knowledge, by learning action models and HTN methods with their preconditions. Unlike other approaches, HierAMLSI is able to learn both actions and methods with noisy and partial inputs observation with a high level or accuracy.\n
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\n \n\n \n \n \n \n \n \n An Efficient HTN to STRIPS Encoding for Concurrent Plans.\n \n \n \n \n\n\n \n Nicolas Cavrel; Damien Pellier; and Humbert Fiorino.\n\n\n \n\n\n\n In Proceedings of the 5th ICAPS Workshop on Hierarchical Planning (HPlan 2022), pages 10–18, 2022. \n \n\n\n\n
\n\n\n\n \n \n \"An paper\n  \n \n \n \"An presentation\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{HPlan2022paper-02,\n  author           = {Nicolas Cavrel and Damien Pellier and Humbert Fiorino},\n  title            = {An Efficient HTN to STRIPS Encoding for Concurrent Plans},\n  booktitle        = {Proceedings of the 5th ICAPS Workshop on Hierarchical Planning (HPlan 2022)},\n  year             = {2022},\n  pages            = {10--18},\n  abstract         = {The Hierarchical Task Network (HTN) formalism is used to express a wide variety of planning problems in terms of decompositions of tasks into subtaks. Many techniques have been proposed to solve such hierarchical planning problems. A particular technique is to encode hierarchical planning problems as classical STRIPS planning problems. One advantage of this technique is to benefit directly from the constant improvements made by STRIPS planners. However, there are still few effective and expressive encodings. In this paper, we present a new HTN to STRIPS encoding allowing to generate concurrent plans. We show experimentally that this encoding outperforms previous approaches on hierarchical IPC benchmarks.},\n  url_paper        = {https://icaps22.icaps-conference.org/workshops/HPlan/papers/paper-02.pdf},\n  url_presentation = {https://youtu.be/Y5pbjClvnFU}\n}\n\n
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\n The Hierarchical Task Network (HTN) formalism is used to express a wide variety of planning problems in terms of decompositions of tasks into subtaks. Many techniques have been proposed to solve such hierarchical planning problems. A particular technique is to encode hierarchical planning problems as classical STRIPS planning problems. One advantage of this technique is to benefit directly from the constant improvements made by STRIPS planners. However, there are still few effective and expressive encodings. In this paper, we present a new HTN to STRIPS encoding allowing to generate concurrent plans. We show experimentally that this encoding outperforms previous approaches on hierarchical IPC benchmarks.\n
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\n \n\n \n \n \n \n \n \n Chronicles for Representing Hierarchical Planning Problems with Time.\n \n \n \n \n\n\n \n Roland Godet; and Arthur Bit-Monnot.\n\n\n \n\n\n\n In Proceedings of the 5th ICAPS Workshop on Hierarchical Planning (HPlan 2022), pages 19–23, 2022. \n \n\n\n\n
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@InProceedings{HPlan2022paper-03,\n  author           = {Roland Godet and Arthur Bit-Monnot},\n  title            = {Chronicles for Representing Hierarchical Planning Problems with Time},\n  booktitle        = {Proceedings of the 5th ICAPS Workshop on Hierarchical Planning (HPlan 2022)},\n  year             = {2022},\n  pages            = {19--23},\n  abstract         = {In temporal planning, chronicles can be used to represent the predictive model of durative actions. Unlike the classical state-oriented representation, the usage of chronicles allows a rich temporal qualification of conditions and effects, beyond the mere start and end times of an action. In this paper we propose an extension of the standard chronicle representation to support hierarchical problems. In particular, we show that the addition of temporally qualified subtasks to chronicles makes them suitable to represent not only primitive actions but also HTN methods. We show how the set of solutions to a chronicle-based hierarchical problem can be quite naturally represented as a Constraint Satisfaction Problem (CSP). To associate semantics to this extended chronicle representation, we propose a set of rules that must hold for any solution to the hierarchical problem, specified as constraints on the associated CSP.},\n  url_paper        = {https://icaps22.icaps-conference.org/workshops/HPlan/papers/paper-03.pdf},\n  url_presentation = {https://youtu.be/J5lCAip3s0w}\n}\n\n
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\n In temporal planning, chronicles can be used to represent the predictive model of durative actions. Unlike the classical state-oriented representation, the usage of chronicles allows a rich temporal qualification of conditions and effects, beyond the mere start and end times of an action. In this paper we propose an extension of the standard chronicle representation to support hierarchical problems. In particular, we show that the addition of temporally qualified subtasks to chronicles makes them suitable to represent not only primitive actions but also HTN methods. We show how the set of solutions to a chronicle-based hierarchical problem can be quite naturally represented as a Constraint Satisfaction Problem (CSP). To associate semantics to this extended chronicle representation, we propose a set of rules that must hold for any solution to the hierarchical problem, specified as constraints on the associated CSP.\n
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\n \n\n \n \n \n \n \n \n Exploiting Solution Order Graphs and Path Decomposition Trees for More Efficient HTN Plan Verification via SAT Solving.\n \n \n \n \n\n\n \n Songtuan Lin; Gregor Behnke; and Pascal Bercher.\n\n\n \n\n\n\n In Proceedings of the 5th ICAPS Workshop on Hierarchical Planning (HPlan 2022), pages 24–28, 2022. \n \n\n\n\n
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@InProceedings{HPlan2022paper-04,\n  author           = {Songtuan Lin and Gregor Behnke and Pascal Bercher},\n  title            = {Exploiting Solution Order Graphs and Path Decomposition Trees for More Efficient HTN Plan Verification via SAT Solving},\n  booktitle        = {Proceedings of the 5th ICAPS Workshop on Hierarchical Planning (HPlan 2022)},\n  year             = {2022},\n  pages            = {24--28},\n  abstract         = {The task of plan verification is to decide whether a given plan is a solution to a planning problem. In this paper, we study the plan verification problem in the context of Hierarchical Task Network (HTN) planning. Concretely, we will develop a new SAT-based approach via exploiting the data structures solution order graphs and path decomposition trees employed by the state-of-the-art SAT-based HTN planner which transforms an HTN plan verification problem into a SAT formula. Additionally, for the purpose of completeness, we will also reimplement the old SAT-based plan verifier within an outdated planning system called PANDA3 and integrate it into the new version called PANDA-pi.},\n  url_paper        = {https://icaps22.icaps-conference.org/workshops/HPlan/papers/paper-04.pdf},\n Xurl_presentation = {}\n}\n\n
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\n The task of plan verification is to decide whether a given plan is a solution to a planning problem. In this paper, we study the plan verification problem in the context of Hierarchical Task Network (HTN) planning. Concretely, we will develop a new SAT-based approach via exploiting the data structures solution order graphs and path decomposition trees employed by the state-of-the-art SAT-based HTN planner which transforms an HTN plan verification problem into a SAT formula. Additionally, for the purpose of completeness, we will also reimplement the old SAT-based plan verifier within an outdated planning system called PANDA3 and integrate it into the new version called PANDA-pi.\n
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\n \n\n \n \n \n \n \n \n Learning Decomposition Methods with Numeric Landmarks and Numeric Preconditions.\n \n \n \n \n\n\n \n Morgan Fine-Morris; Michael W. Floyd; Bryan Auslander; Greg Pennisi; Kalyan Gupta; Mark Roberts; Jeff Heflin; and Héctor Muñoz-Avila.\n\n\n \n\n\n\n In Proceedings of the 5th ICAPS Workshop on Hierarchical Planning (HPlan 2022), pages 29–37, 2022. \n \n\n\n\n
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@InProceedings{HPlan2022paper-05,\n  author           = {Morgan Fine-Morris and Michael W. Floyd and Bryan Auslander and Greg Pennisi and Kalyan Gupta and Mark Roberts and Jeff Heflin and Héctor Muñoz-Avila},\n  title            = {Learning Decomposition Methods with Numeric Landmarks and Numeric Preconditions},\n  booktitle        = {Proceedings of the 5th ICAPS Workshop on Hierarchical Planning (HPlan 2022)},\n  year             = {2022},\n  pages            = {29--37},\n  abstract         = {We describe an HTN method-learning system, which we call\nT2N, that learns hierarchical structure from plan traces in domains with numeric effects, where some subgoals are numeric. We investigate how different methods of preprocessing training data can impact the effectiveness of the learned methods. We test the learned methods by solving a set of 30 test problems in a simple numeric crafting domain based on the videogame Minecraft. Our results indicate that we can learn functional methods for domains with these characteristics and suggest that different preprocessing techniques lead to method sets with different strengths and weaknesses, with no preprocessing technique superior across all domain tasks.},\n  url_paper        = {https://icaps22.icaps-conference.org/workshops/HPlan/papers/paper-05.pdf},\n  url_presentation = {https://youtu.be/NhDfL7YC6pQ}\n}\n\n
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\n We describe an HTN method-learning system, which we call T2N, that learns hierarchical structure from plan traces in domains with numeric effects, where some subgoals are numeric. We investigate how different methods of preprocessing training data can impact the effectiveness of the learned methods. We test the learned methods by solving a set of 30 test problems in a simple numeric crafting domain based on the videogame Minecraft. Our results indicate that we can learn functional methods for domains with these characteristics and suggest that different preprocessing techniques lead to method sets with different strengths and weaknesses, with no preprocessing technique superior across all domain tasks.\n
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\n \n\n \n \n \n \n \n \n Learning Operational Models from Demonstrations: Parameterization and Model Quality Evaluation.\n \n \n \n \n\n\n \n Philippe Hérail; and Arthur Bit-Monnot.\n\n\n \n\n\n\n In Proceedings of the 5th ICAPS Workshop on Hierarchical Planning (HPlan 2022), pages 38–46, 2022. \n \n\n\n\n
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@InProceedings{HPlan2022paper-06,\n  author           = {Philippe Hérail and Arthur Bit-Monnot},\n  title            = {Learning Operational Models from Demonstrations: Parameterization and Model Quality Evaluation},\n  booktitle        = {Proceedings of the 5th ICAPS Workshop on Hierarchical Planning (HPlan 2022)},\n  year             = {2022},\n  pages            = {38--46},\n  abstract         = {When acting in non-deterministic environments, autonomous agents must balance between long-term, complex goals with unpredictable events and reactive behavior. In this context, hierarchical operational models are attractive in that they allow the execution of complex behavior either in a purely reactive fashion or guided by a planning process. Just like for HTN models with which they share most characteristics, one key bottleneck in the exploitation of operational models is their acquisition. In this paper, we introduce an algorithm for learning hierarchical operational models from a set of demonstrations. Given an initial vocabulary of tasks and some demonstrations of how they could be achieved, we present how each task can be associated to a set of methods capturing the operational knowledge of how it can be achieved. We present the structure of the learned models, the algorithm used to learn them as well as a preliminary evaluation of this algorithm.},\n  url_paper        = {https://icaps22.icaps-conference.org/workshops/HPlan/papers/paper-06.pdf},\n  url_presentation = {https://youtu.be/IX_PIuqfFqE}\n}\n\n
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\n When acting in non-deterministic environments, autonomous agents must balance between long-term, complex goals with unpredictable events and reactive behavior. In this context, hierarchical operational models are attractive in that they allow the execution of complex behavior either in a purely reactive fashion or guided by a planning process. Just like for HTN models with which they share most characteristics, one key bottleneck in the exploitation of operational models is their acquisition. In this paper, we introduce an algorithm for learning hierarchical operational models from a set of demonstrations. Given an initial vocabulary of tasks and some demonstrations of how they could be achieved, we present how each task can be associated to a set of methods capturing the operational knowledge of how it can be achieved. We present the structure of the learned models, the algorithm used to learn them as well as a preliminary evaluation of this algorithm.\n
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\n \n\n \n \n \n \n \n \n On the Efficient Inference of Preconditions and Effects of Compound Tasks in Partially Ordered HTN Planning Domains.\n \n \n \n \n\n\n \n Conny Olz; and Pascal Bercher.\n\n\n \n\n\n\n In Proceedings of the 5th ICAPS Workshop on Hierarchical Planning (HPlan 2022), pages 47–51, 2022. \n \n\n\n\n
\n\n\n\n \n \n \"On paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 15 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{HPlan2022paper-07,\n  author           = {Conny Olz and Pascal Bercher},\n  title            = {On the Efficient Inference of Preconditions and Effects of Compound Tasks in\nPartially Ordered HTN Planning Domains},\n  booktitle        = {Proceedings of the 5th ICAPS Workshop on Hierarchical Planning (HPlan 2022)},\n  year             = {2022},\n  pages            = {47--51},\n  abstract         = {Recently, preconditions and effects of compound tasks based on their possible refinements have been introduced together with an efficient inference procedure to compute a subset of them. However, they were restricted to total-order HTN planning domains. In this paper we generalize the definitions and algorithm to the scenario of partially ordered domains.},\n  url_paper        = {https://icaps22.icaps-conference.org/workshops/HPlan/papers/paper-07.pdf}\n}\n\n
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\n Recently, preconditions and effects of compound tasks based on their possible refinements have been introduced together with an efficient inference procedure to compute a subset of them. However, they were restricted to total-order HTN planning domains. In this paper we generalize the definitions and algorithm to the scenario of partially ordered domains.\n
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\n \n\n \n \n \n \n \n \n On Total-Order HTN Plan Verification with Method Preconditions – An Extension of the CYK Parsing Algorithm.\n \n \n \n \n\n\n \n Songtuan Lin; Gregor Behnke; Simona Ondrčková; Roman Barták; and Pascal Bercher.\n\n\n \n\n\n\n In Proceedings of the 5th ICAPS Workshop on Hierarchical Planning (HPlan 2022), pages 52–58, 2022. \n \n\n\n\n
\n\n\n\n \n \n \"On paper\n  \n \n \n \"On presentation\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 19 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{HPlan2022paper-08,\n  author           = {Songtuan Lin and Gregor Behnke and Simona Ondrčková and Roman Barták and Pascal Bercher},\n  title            = {On Total-Order HTN Plan Verification with Method Preconditions -- An Extension of the CYK Parsing Algorithm},\n  booktitle        = {Proceedings of the 5th ICAPS Workshop on Hierarchical Planning (HPlan 2022)},\n  year             = {2022},\n  pages            = {52--58},\n  abstract         = {In this paper, we consider the plan verification problem for totally ordered (TO) HTN planning. The problem is proved to be solvable in polynomial time by recognizing its connection to the membership decision problem for context-free grammars. Currently, most HTN plan verification approaches do not have special treatments for the TO configuration, and the only one features such an optimization still relies on an exhaustive search. Hence, we will develop a new TOHTN plan verification approach in this paper by extending the standard CYK parsing algorithm which acts as the best decision procedure in general.},\n  url_paper        = {https://icaps22.icaps-conference.org/workshops/HPlan/papers/paper-08.pdf},\n  url_presentation = {https://youtu.be/AJqmbG9Bs2M}\n}\n\n
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\n In this paper, we consider the plan verification problem for totally ordered (TO) HTN planning. The problem is proved to be solvable in polynomial time by recognizing its connection to the membership decision problem for context-free grammars. Currently, most HTN plan verification approaches do not have special treatments for the TO configuration, and the only one features such an optimization still relies on an exhaustive search. Hence, we will develop a new TOHTN plan verification approach in this paper by extending the standard CYK parsing algorithm which acts as the best decision procedure in general.\n
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\n \n\n \n \n \n \n \n \n T-HTN: Timeline Based HTN Planning for Multiple Robots.\n \n \n \n \n\n\n \n Viraj Parimi; Zachary B. Rubinstein; and Stephen F. Smith.\n\n\n \n\n\n\n In Proceedings of the 5th ICAPS Workshop on Hierarchical Planning (HPlan 2022), pages 59–67, 2022. \n \n\n\n\n
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@InProceedings{HPlan2022paper-09,\n  author           = {Viraj Parimi and Zachary B. Rubinstein and Stephen F. Smith},\n  title            = {T-HTN: Timeline Based HTN Planning for Multiple Robots},\n  booktitle        = {Proceedings of the 5th ICAPS Workshop on Hierarchical Planning (HPlan 2022)},\n  year             = {2022},\n  pages            = {59--67},\n  abstract         = {Effective coordinated actions by a team of robots operating in close proximity to one another is an important requirement in many emerging applications, ranging from warehousing and material movement to the conduct of autonomous housekeeping and maintenance of deep space habitats during unmanned periods. Yet, such multi-robot planning problems remain a significant challenge for contemporary planning technologies, due to several complicating factors: goals must be assigned to robots and accomplished over time in the presence of complex temporal and spatial constraints in a manner that optimizes overall team performance, attention must be given to the durational uncertainty inherent in robot task execution, and planning must be responsive to changing and unexpected execution circumstances. In this paper, we present T-HTN, a novel planner that attempts to overcome this challenge by coupling the structure and efficiency of Hierarchical Task Network (HTN) models with the flexible scheduling infrastructure of timeline-based planning systems. We present initial results on a simple set of multi-robot problems that show the potential of T-HTN in comparison to a state-of-the-art PDDL-style temporal planner.},\n  url_paper        = {https://icaps22.icaps-conference.org/workshops/HPlan/papers/paper-09.pdf},\n  url_presentation = {https://youtu.be/eGNyj5lOrXY}\n}\n\n
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\n Effective coordinated actions by a team of robots operating in close proximity to one another is an important requirement in many emerging applications, ranging from warehousing and material movement to the conduct of autonomous housekeeping and maintenance of deep space habitats during unmanned periods. Yet, such multi-robot planning problems remain a significant challenge for contemporary planning technologies, due to several complicating factors: goals must be assigned to robots and accomplished over time in the presence of complex temporal and spatial constraints in a manner that optimizes overall team performance, attention must be given to the durational uncertainty inherent in robot task execution, and planning must be responsive to changing and unexpected execution circumstances. In this paper, we present T-HTN, a novel planner that attempts to overcome this challenge by coupling the structure and efficiency of Hierarchical Task Network (HTN) models with the flexible scheduling infrastructure of timeline-based planning systems. We present initial results on a simple set of multi-robot problems that show the potential of T-HTN in comparison to a state-of-the-art PDDL-style temporal planner.\n
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\n \n\n \n \n \n \n \n \n Teaching an HTN Learner.\n \n \n \n \n\n\n \n Ruoxi Li; Mark Roberts; Morgan Fine-Morris; and Dana Nau.\n\n\n \n\n\n\n In Proceedings of the 5th ICAPS Workshop on Hierarchical Planning (HPlan 2022), pages 68–72, 2022. \n \n\n\n\n
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@InProceedings{HPlan2022paper-10,\n  author           = {Ruoxi Li and Mark Roberts and Morgan Fine-Morris and Dana Nau},\n  title            = {Teaching an HTN Learner},\n  booktitle        = {Proceedings of the 5th ICAPS Workshop on Hierarchical Planning (HPlan 2022)},\n  year             = {2022},\n  pages            = {68--72},\n  abstract         = {We describe Teachable-HTN-Maker, a modified version of the well-known HTN-Maker algorithm that learns Hierarchical Task Network (HTN) methods. Instead of learning methods from all subsequences of a solution plan as HTN-Maker does, Teachable-HTN-Maker learns from a curriculum consisting of examples that are presented in a meaningful order. We compare Teachable-HTN-Maker against HTN-Maker in two planning domains, and observe that it learns fewer methods and better ones.},\n  url_paper        = {https://icaps22.icaps-conference.org/workshops/HPlan/papers/paper-10.pdf},\n  url_presentation = {https://youtu.be/6CdCTGvBwvM}\n}\n\n
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\n We describe Teachable-HTN-Maker, a modified version of the well-known HTN-Maker algorithm that learns Hierarchical Task Network (HTN) methods. Instead of learning methods from all subsequences of a solution plan as HTN-Maker does, Teachable-HTN-Maker learns from a curriculum consisting of examples that are presented in a meaningful order. We compare Teachable-HTN-Maker against HTN-Maker in two planning domains, and observe that it learns fewer methods and better ones.\n
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\n \n\n \n \n \n \n \n \n Urban Modeling via Hierarchical Task Network Planning.\n \n \n \n \n\n\n \n Michael Staud.\n\n\n \n\n\n\n In Proceedings of the 5th ICAPS Workshop on Hierarchical Planning (HPlan 2022), pages 73–77, 2022. \n \n\n\n\n
\n\n\n\n \n \n \"Urban paper\n  \n \n \n \"Urban presentation\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{HPlan2022paper-11,\n  author           = {Michael Staud},\n  title            = {Urban Modeling via Hierarchical Task Network Planning},\n  booktitle        = {Proceedings of the 5th ICAPS Workshop on Hierarchical Planning (HPlan 2022)},\n  year             = {2022},\n  pages            = {73--77},\n  abstract         = {In this paper we present a new method for city modeling based on hierarchical task network planning. The planner creates actions that are executed in a city simulation. These actions generate step by step a city model within the simulation. The advantage of this approach is that it takes into account that real cities are not designed on a drawing board, but have a history of development. By simulating this development, economic aspects can be taken into account. The result is a much more realistic urban model. An urban simulation is an extremely complex planning domain for a planner. Therefore, we have developed a new domain-independent hierarchical task network planning algorithm that divides the planning problem into smaller planning problems. Our planning algorithm is sound and complete. We give preliminary results on its performance.},\n  url_paper        = {https://icaps22.icaps-conference.org/workshops/HPlan/papers/paper-11.pdf},\n  url_presentation = {https://youtu.be/xK8_c2-SFAw}\n}\n\n
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\n In this paper we present a new method for city modeling based on hierarchical task network planning. The planner creates actions that are executed in a city simulation. These actions generate step by step a city model within the simulation. The advantage of this approach is that it takes into account that real cities are not designed on a drawing board, but have a history of development. By simulating this development, economic aspects can be taken into account. The result is a much more realistic urban model. An urban simulation is an extremely complex planning domain for a planner. Therefore, we have developed a new domain-independent hierarchical task network planning algorithm that divides the planning problem into smaller planning problems. Our planning algorithm is sound and complete. We give preliminary results on its performance.\n
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\n \n\n \n \n \n \n \n \n Towards Hierarchical Task Network Planning as Constraint Satisfaction Problem.\n \n \n \n \n\n\n \n Tobias Schwartz; Michael Sioutis; and Diedrich Wolter.\n\n\n \n\n\n\n In Proceedings of the 5th ICAPS Workshop on Hierarchical Planning (HPlan 2022), pages 78–82, 2022. \n This is a challenge paper.\n\n\n\n
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@InProceedings{HPlan2022paper-12,\n  author           = {Tobias Schwartz and Michael Sioutis and Diedrich Wolter},\n  title            = {Towards Hierarchical Task Network Planning as Constraint Satisfaction Problem},\n  booktitle        = {Proceedings of the 5th ICAPS Workshop on Hierarchical Planning (HPlan 2022)},\n  year             = {2022},\n  pages            = {78--82},\n  note             = {This is a challenge paper.},\n  abstract         = {In recent years, propositional logic encodings for HTN planning have seen many improvements and resulted in competitive planners. Modeling all kinds of features and constraints imposed by the task hierarchy, however, is very challenging in propositional logic, and has recently led to including preprocessing steps before creating the SAT formulas. Instead of using propositional logic, classical planning problems have previously been encoded as constraint satisfaction problems (CSPs), which are more expressive. Indeed, CSPs allow a more natural and convenient way of representing all constraints of the task hierarchy, yet only little work on using constraint solving methods in HTN planning exist. Hence, in this paper, we outline first ideas for encoding an HTN planning problem into a single CSP. Our motivation lies in obtaining constraint networks for HTN planning that we can solve with state-of-the-art solvers.},\n  url_paper        = {https://icaps22.icaps-conference.org/workshops/HPlan/papers/paper-12.pdf},\n  url_presentation = {https://youtu.be/X-OTsayX-ck}\n}\n\n\n\n\n\n\n\n
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\n In recent years, propositional logic encodings for HTN planning have seen many improvements and resulted in competitive planners. Modeling all kinds of features and constraints imposed by the task hierarchy, however, is very challenging in propositional logic, and has recently led to including preprocessing steps before creating the SAT formulas. Instead of using propositional logic, classical planning problems have previously been encoded as constraint satisfaction problems (CSPs), which are more expressive. Indeed, CSPs allow a more natural and convenient way of representing all constraints of the task hierarchy, yet only little work on using constraint solving methods in HTN planning exist. Hence, in this paper, we outline first ideas for encoding an HTN planning problem into a single CSP. Our motivation lies in obtaining constraint networks for HTN planning that we can solve with state-of-the-art solvers.\n
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\n  \n 2021\n \n \n (14)\n \n \n
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\n \n\n \n \n \n \n \n \n Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021).\n \n \n \n \n\n\n \n Pascal Bercher; Jane Jean Kiam; Zhanhao Xiao; and Ron Alford.,\n editors.\n \n\n\n \n\n\n\n 2021.\n \n\n\n\n
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@Proceedings{HPlan2021proceedings,\n  editor          = {Pascal Bercher and Jane Jean Kiam and Zhanhao Xiao and Ron Alford},\n  title           = {Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021)},\n  year            = {2021},\n  url_website     = {http://hplan2021.hierarchical-task.net},\n  url_proceedings = {https://hierarchical-task.net/publications/hplan/HPlanProceedings-2021.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n \n A Hierarchical Approach to Multi-Agent Path Finding.\n \n \n \n \n\n\n \n Han Zhang; Mingze Yao; Ziang Liu; Jiaoyang Li; Lucas Terr; Shao-Hung Chan; T. K. Satish Kumar; and Sven Koenig.\n\n\n \n\n\n\n In Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021), pages 1–7, 2021. \n \n\n\n\n
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@InProceedings{HPlan2021paper1,\n  author           = {Han Zhang and Mingze Yao and Ziang Liu and Jiaoyang Li and Lucas Terr and Shao-Hung Chan and T. K. Satish Kumar and Sven Koenig},\n  title            = {A Hierarchical Approach to Multi-Agent Path Finding},\n  booktitle        = {Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021)},\n  year             = {2021},\n  pages            = {1--7},\n  abstract         = {The Multi-Agent Path Finding (MAPF) problem arises in many real-world applications, ranging from automated warehousing to multi-drone delivery. Solving the MAPF problem optimally is NP-hard, and existing optimal and bounded-suboptimal MAPF solvers thus usually do not scale to large MAPF instances. Greedy MAPF solvers scale to large MAPF instances, but their solution qualities are often bad. In this paper, we therefore propose a novel MAPF solver, Hierarchical Multi-Agent Path Planner (HMAPP), which creates a spatial hierarchy by partitioning the environment into multiple regions and decomposes a MAPF instance into smaller MAPF sub-instances for each region. For each sub-instance, it uses a bounded-suboptimal MAPF solver to solve it with good solution quality. Our experimental results show that HMAPP solves as large MAPF instances as greedy MAPF solvers while achieving better solution qualities on various maps.},\n  url_paper        = {https://hierarchical-task.net/publications/hplan/2021/HPlan2021-paper1.pdf},\n  url_presentation = {https://www.youtube.com/watch?v=eBZALSeZpXU&t=3s}\n}\n\n
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\n The Multi-Agent Path Finding (MAPF) problem arises in many real-world applications, ranging from automated warehousing to multi-drone delivery. Solving the MAPF problem optimally is NP-hard, and existing optimal and bounded-suboptimal MAPF solvers thus usually do not scale to large MAPF instances. Greedy MAPF solvers scale to large MAPF instances, but their solution qualities are often bad. In this paper, we therefore propose a novel MAPF solver, Hierarchical Multi-Agent Path Planner (HMAPP), which creates a spatial hierarchy by partitioning the environment into multiple regions and decomposes a MAPF instance into smaller MAPF sub-instances for each region. For each sub-instance, it uses a bounded-suboptimal MAPF solver to solve it with good solution quality. Our experimental results show that HMAPP solves as large MAPF instances as greedy MAPF solvers while achieving better solution qualities on various maps.\n
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\n \n\n \n \n \n \n \n \n Compiling HTN Plan Verification Problems into HTN Planning Problems.\n \n \n \n \n\n\n \n Daniel Höller; Julia Wichlacz; Pascal Bercher; and Gregor Behnke.\n\n\n \n\n\n\n In Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021), pages 8–15, 2021. \n \n\n\n\n
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@InProceedings{HPlan2021paper2,\n  author           = {Daniel H\\"{o}ller and Julia Wichlacz and Pascal Bercher and Gregor Behnke},\n  title            = {Compiling HTN Plan Verification Problems into HTN Planning Problems},\n  booktitle        = {Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021)},\n  year             = {2021},\n  pages            = {8--15},\n  abstract         = {Plan Verification is the task of deciding whether a sequence of actions is a solution for a given planning problem. In HTN planning, the task is computationally expensive and may be up to NP-hard. However, there are situations where it needs to be solved, e.g. when a solution is post-processed, in systems using approximation, or just to validate whether a planning system works correctly (e.g. for debugging or in a competition). In the literature, there are verification systems based on translations to propositional logic and based on techniques from parsing. Here we present a third approach and translate HTN plan verification problems into HTN planning problems. These can be solved using any HTN planning system. We test our solver on the set of solutions from the 2020 International Planning Competition. Our evaluation is yet preliminary, because it does not include all systems from the literature, but it already shows that our approach performs well compared with the included systems.},\n  url_paper        = {https://hierarchical-task.net/publications/hplan/2021/HPlan2021-paper2.pdf},\n  url_presentation = {https://www.youtube.com/watch?v=SkVUtQJlVZc}\n}\n\n
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\n Plan Verification is the task of deciding whether a sequence of actions is a solution for a given planning problem. In HTN planning, the task is computationally expensive and may be up to NP-hard. However, there are situations where it needs to be solved, e.g. when a solution is post-processed, in systems using approximation, or just to validate whether a planning system works correctly (e.g. for debugging or in a competition). In the literature, there are verification systems based on translations to propositional logic and based on techniques from parsing. Here we present a third approach and translate HTN plan verification problems into HTN planning problems. These can be solved using any HTN planning system. We test our solver on the set of solutions from the 2020 International Planning Competition. Our evaluation is yet preliminary, because it does not include all systems from the literature, but it already shows that our approach performs well compared with the included systems.\n
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\n \n\n \n \n \n \n \n \n Domain Analysis: A Preprocessing Method that Reduces the Size of the Search Tree in Hybrid Planning.\n \n \n \n \n\n\n \n Michael Staud.\n\n\n \n\n\n\n In Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021), pages 16–20, 2021. \n \n\n\n\n
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@InProceedings{HPlan2021paper3,\n  author           = {Michael Staud},\n  title            = {Domain Analysis: A Preprocessing Method that Reduces the Size of the Search Tree in Hybrid Planning},\n  booktitle        = {Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021)},\n  year             = {2021},\n  pages            = {16--20},\n  abstract         = {We introduce a new method that can reduce the size of the search tree in hybrid planning. Hybrid planning fuses task insertion HTN planning with POCL planning. As planning is a computationally difficult problem, the size of the search tree can grow exponentially to the size of the problem.<br/>\n  We create so-called plan templates in a preprocessing step of the domain. They can be used to replace an abstract task in a partial plan. This task then no longer needs to be decomposed.<br/>\n  We provide empirical evidence in favor of this approach and show that the use of plan templates can drastically reduce the size of the search tree in hybrid planners. We use a PANDA-like planner as a testbed and publicly available planning domains to verify our claims.},\n  url_paper        = {https://hierarchical-task.net/publications/hplan/2021/HPlan2021-paper3.pdf},\n  url_presentation = {https://www.youtube.com/watch?v=u3UE3-txp1o}\n}\n\n
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\n We introduce a new method that can reduce the size of the search tree in hybrid planning. Hybrid planning fuses task insertion HTN planning with POCL planning. As planning is a computationally difficult problem, the size of the search tree can grow exponentially to the size of the problem.
We create so-called plan templates in a preprocessing step of the domain. They can be used to replace an abstract task in a partial plan. This task then no longer needs to be decomposed.
We provide empirical evidence in favor of this approach and show that the use of plan templates can drastically reduce the size of the search tree in hybrid planners. We use a PANDA-like planner as a testbed and publicly available planning domains to verify our claims.\n
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\n \n\n \n \n \n \n \n \n GTPyhop: A Hierarchical Goal+Task Planner Implemented in Python.\n \n \n \n \n\n\n \n Dana Nau; Yash Bansod; Sunandita Patra; Mark Roberts; and Ruoxi Li.\n\n\n \n\n\n\n In Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021), pages 21–25, 2021. \n \n\n\n\n
\n\n\n\n \n \n \"GTPyhop: paper\n  \n \n \n \"GTPyhop: presentation\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{HPlan2021paper4,\n  author           = {Dana Nau and Yash Bansod and Sunandita Patra and Mark Roberts and Ruoxi Li},\n  title            = {GTPyhop: A Hierarchical Goal+Task Planner Implemented in Python},\n  booktitle        = {Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021)},\n  year             = {2021},\n  pages            = {21--25},\n  abstract         = {The Pyhop planner, released in 2013, was a simple SHOP-style planner written in Python. It was designed to be easily usable as an embedded system in conventional applications such as game programs. Although little effort was made to publicize Pyhop, its simplicity, ease of use, and understandability led to its use in a number of projects beyond its original intent, and to publications by others.<br/>\n  GTPyhop (Goal-and-Task Pyhop) is an extended version of Pyhop that can plan for both goals and tasks, using a combination of SHOP-style task decomposition and GDP-style goal decomposition. It provides a totally-ordered version of Goal-Task-Network (GTN) planning without sharing and task insertion. GTPyhop’s ability to represent and reason about both goals and tasks provides a high degree of flexibility for representing objectives in whichever form seems more natural to the domain designer.},\n  url_paper        = {https://hierarchical-task.net/publications/hplan/2021/HPlan2021-paper4.pdf},\n  url_presentation = {https://www.youtube.com/watch?v=O_xmFvPd1wE}\n}\n\n
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\n The Pyhop planner, released in 2013, was a simple SHOP-style planner written in Python. It was designed to be easily usable as an embedded system in conventional applications such as game programs. Although little effort was made to publicize Pyhop, its simplicity, ease of use, and understandability led to its use in a number of projects beyond its original intent, and to publications by others.
GTPyhop (Goal-and-Task Pyhop) is an extended version of Pyhop that can plan for both goals and tasks, using a combination of SHOP-style task decomposition and GDP-style goal decomposition. It provides a totally-ordered version of Goal-Task-Network (GTN) planning without sharing and task insertion. GTPyhop’s ability to represent and reason about both goals and tasks provides a high degree of flexibility for representing objectives in whichever form seems more natural to the domain designer.\n
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\n \n\n \n \n \n \n \n \n Integrating Planning and Acting With a Re-Entrant HTN Planner.\n \n \n \n \n\n\n \n Yash Bansod; Dana Nau; Sunandita Patra; and Mak Roberts.\n\n\n \n\n\n\n In Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021), pages 26–34, 2021. \n \n\n\n\n
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@InProceedings{HPlan2021paper5,\n  author    = {Yash Bansod and Dana Nau and Sunandita Patra and Mak Roberts},\n  title     = {Integrating Planning and Acting With a Re-Entrant HTN Planner},\n  booktitle = {Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021)},\n  year      = {2021},\n  pages     = {26--34},\n  abstract  = {A major problem with integrating HTN planning and acting is that, unless the HTN methods are very carefully written, unexpected problems can occur when attempting to replan if execution errors or other unexpected conditions occur during acting. To overcome this problem, we present a re-entrant HTN planning algorithm that can be restarted for replanning purposes at the point where an execution error occurred, and an HTN acting algorithm that can restart the HTN planner at this point. We show through experiments that our algorithm is an improvement over a widely used approach to planning and control.},\n  url_paper = {https://hierarchical-task.net/publications/hplan/2021/HPlan2021-paper5.pdf},\n  url_presentation = {https://www.youtube.com/watch?v=pT8WJXoR_Jo}\n}\n\n\n
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\n A major problem with integrating HTN planning and acting is that, unless the HTN methods are very carefully written, unexpected problems can occur when attempting to replan if execution errors or other unexpected conditions occur during acting. To overcome this problem, we present a re-entrant HTN planning algorithm that can be restarted for replanning purposes at the point where an execution error occurred, and an HTN acting algorithm that can restart the HTN planner at this point. We show through experiments that our algorithm is an improvement over a widely used approach to planning and control.\n
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\n \n\n \n \n \n \n \n \n On the Computational Complexity of Correcting HTN Domain Models.\n \n \n \n \n\n\n \n Songtuan Lin; and Pascal Bercher.\n\n\n \n\n\n\n In Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021), pages 35–43, 2021. \n \n\n\n\n
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@InProceedings{HPlan2021paper6,\n  author           = {Songtuan Lin and Pascal Bercher},\n  title            = {On the Computational Complexity of Correcting HTN Domain Models},\n  booktitle        = {Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021)},\n  year             = {2021},\n  pages            = {35--43},\n  abstract         = {Incorporating user requests into planning processes is a key concept in developing flexible planning technologies. Such systems may be required to change its planning model to adapt to certain user requests. In this paper, we assume a user provides a non-solution plan to a system and asks it to change the planning model so that the plan becomes a solution. We study the computational complexity of deciding whether such changes exist in the context of Hierarchical Task Network (HTN) planning. We prove that the problem is NP-complete in general independent of what or how many changes are allowed. We also identify several conditions which make the problem tractable when they are satisfied.},\n  url_paper        = {https://hierarchical-task.net/publications/hplan/2021/HPlan2021-paper6.pdf},\n  url_presentation = {https://www.youtube.com/watch?v=05DE4U6_Qh8}\n}\n\n\n
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\n Incorporating user requests into planning processes is a key concept in developing flexible planning technologies. Such systems may be required to change its planning model to adapt to certain user requests. In this paper, we assume a user provides a non-solution plan to a system and asks it to change the planning model so that the plan becomes a solution. We study the computational complexity of deciding whether such changes exist in the context of Hierarchical Task Network (HTN) planning. We prove that the problem is NP-complete in general independent of what or how many changes are allowed. We also identify several conditions which make the problem tractable when they are satisfied.\n
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\n \n\n \n \n \n \n \n \n On the Verification of Totally-Ordered HTN Plans.\n \n \n \n \n\n\n \n Roman Barták; Simona Ondrčková; Gregor Behnke; and Pascal Bercher.\n\n\n \n\n\n\n In Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021), pages 44–48, 2021. \n A follow-up paper was later accepted at ICTAI 2021.\n\n\n\n
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@InProceedings{HPlan2021paper7,\n  author           = {Roman Bart{\\'a}k and Simona Ondr\\v{c}kov\\'{a} and Gregor Behnke and Pascal Bercher},\n  title            = {On the Verification of Totally-Ordered HTN Plans},\n  booktitle        = {Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021)},\n  year             = {2021},\n  pages            = {44--48},\n  abstract         = {Verifying HTN plans is an intractable problem with two existing approaches to solve the problem. One technique is based on compilation to SAT. Another method is using parsing, and it is currently the fastest technique for verifying HTN plans. In this paper, we propose an extension of the parsing-based approach to verify totally-ordered HTN plans more efficiently. This problem is known to be tractable if no state constraints are included, and we show theoretically and empirically that the modified parsing approach achieves better performance than the currently fastest HTN plan verifier when applied to totally-ordered HTN plans.},\n  url_paper        = {https://hierarchical-task.net/publications/hplan/2021/HPlan2021-paper7.pdf},\n  note             = {A follow-up paper was later accepted at ICTAI 2021.},\n  url_presentation = {https://www.youtube.com/watch?v=zqUOjWru-TE}\n}\n\n
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\n Verifying HTN plans is an intractable problem with two existing approaches to solve the problem. One technique is based on compilation to SAT. Another method is using parsing, and it is currently the fastest technique for verifying HTN plans. In this paper, we propose an extension of the parsing-based approach to verify totally-ordered HTN plans more efficiently. This problem is known to be tractable if no state constraints are included, and we show theoretically and empirically that the modified parsing approach achieves better performance than the currently fastest HTN plan verifier when applied to totally-ordered HTN plans.\n
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\n \n\n \n \n \n \n \n \n Solving Hierarchical Auctions with HTN Planning.\n \n \n \n \n\n\n \n Antoine Milot; Estelle Chauveau; Simon Lacroix; and Charles Lesire.\n\n\n \n\n\n\n In Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021), pages 49–56, 2021. \n \n\n\n\n
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@InProceedings{HPlan2021paper8,\n  author           = {Antoine Milot and Estelle Chauveau and Simon Lacroix and Charles Lesire},\n  title            = {Solving Hierarchical Auctions with HTN Planning},\n  booktitle        = {Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021)},\n  year             = {2021},\n  pages            = {49--56},\n  abstract         = {This paper presents a preliminary approach to solve the Multi-Robot Task Allocation problem through hierarchical auctions combined with the use of HTN planning. We present the global approach and the challenges arisen by partially-ordered HTNs through some examples. We finally outline some options to integrate such constraints in the allocation scheme.},\n  url_paper        = {https://hierarchical-task.net/publications/hplan/2021/HPlan2021-paper8.pdf}\n}\n\n
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\n This paper presents a preliminary approach to solve the Multi-Robot Task Allocation problem through hierarchical auctions combined with the use of HTN planning. We present the global approach and the challenges arisen by partially-ordered HTNs through some examples. We finally outline some options to integrate such constraints in the allocation scheme.\n
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\n \n\n \n \n \n \n \n \n Solving POMDPs online through HTN Planning and Monte Carlo Tree Search.\n \n \n \n \n\n\n \n Robert P. Goldman.\n\n\n \n\n\n\n In Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021), pages 57–61, 2021. \n \n\n\n\n
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@InProceedings{HPlan2021paper9,\n  author           = {Robert P. Goldman},\n  title            = {Solving POMDPs online through HTN Planning and Monte Carlo Tree Search},\n  booktitle        = {Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021)},\n  year             = {2021},\n  pages            = {57--61},\n  abstract         = {This paper describes our SHOPPINGS PREE HTN algorithm for online planning in Partially Observable Markov Decision Processes (POMDPs). SHOPPING S PREE combines the HTN planning algorithm from S HOP 3, extensions to SHOP3’s representation to handle partial observability, and Monte Carlo Tree Search for efficient sampling in the problem space. This paper presents only the algorithm and initial notes on the implementation: this is work in progress.},\n  url_paper        = {https://hierarchical-task.net/publications/hplan/2021/HPlan2021-paper9.pdf},\n  url_presentation = {https://www.youtube.com/watch?v=YMls2DbkS_A}\n}\n\n
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\n This paper describes our SHOPPINGS PREE HTN algorithm for online planning in Partially Observable Markov Decision Processes (POMDPs). SHOPPING S PREE combines the HTN planning algorithm from S HOP 3, extensions to SHOP3’s representation to handle partial observability, and Monte Carlo Tree Search for efficient sampling in the problem space. This paper presents only the algorithm and initial notes on the implementation: this is work in progress.\n
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\n \n\n \n \n \n \n \n \n The Complexity of Flexible FOND HTN Planning.\n \n \n \n \n\n\n \n Dillon Chen; and Pascal Bercher.\n\n\n \n\n\n\n In Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021), pages 62–70, 2021. \n \n\n\n\n
\n\n\n\n \n \n \"The paper\n  \n \n \n \"The presentation\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{HPlan2021paper11,\n  author           = {Dillon Chen and Pascal Bercher},\n  title            = {The Complexity of Flexible FOND HTN Planning},\n  booktitle        = {Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021)},\n  year             = {2021},\n  pages            = {62--70},\n  abstract         = {Hierarchical Task Network (HTN) planning is an expressive planning formalism that has often been advocated as a first choice to address real-world problems. Yet only a few extensions exist that can deal with the many challenges encountered in the real world. One of them is the capability to express uncertainty. Recently, a new HTN formalism for Fully Observable Nondeterministic (FOND) problems was proposed and studied theoretically. In this paper, we lay out limitations of that formalism and propose an alternative definition, which addresses and resolves such limitations. We conduct a complexity study of an alternative, more flexible formalism and provide tight complexity bounds for most of the investigated special cases of the problem.},\n  url_paper        = {https://hierarchical-task.net/publications/hplan/2021/HPlan2021-paper11.pdf},\n  url_presentation = {https://www.youtube.com/watch?v=RtmJ0djVi-8}\n}\n\n
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\n Hierarchical Task Network (HTN) planning is an expressive planning formalism that has often been advocated as a first choice to address real-world problems. Yet only a few extensions exist that can deal with the many challenges encountered in the real world. One of them is the capability to express uncertainty. Recently, a new HTN formalism for Fully Observable Nondeterministic (FOND) problems was proposed and studied theoretically. In this paper, we lay out limitations of that formalism and propose an alternative definition, which addresses and resolves such limitations. We conduct a complexity study of an alternative, more flexible formalism and provide tight complexity bounds for most of the investigated special cases of the problem.\n
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\n \n\n \n \n \n \n \n \n Temporal Hierarchical Task Network Planning with Nested Multi-Vehicle Routing Problems – A Challenge to be Resolved.\n \n \n \n \n\n\n \n Jane Jean Kiam; Pascal Bercher; and Axel Schulte.\n\n\n \n\n\n\n In Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021), pages 71–75, 2021. \n This is a challenge paper.\n\n\n\n
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@InProceedings{HPlan2021paper12,\n  author           = {Jane Jean Kiam and Pascal Bercher and Axel Schulte},\n  title            = {Temporal Hierarchical Task Network Planning with Nested Multi-Vehicle Routing Problems -- A Challenge to be Resolved},\n  booktitle        = {Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021)},\n  year             = {2021},\n  pages            = {71--75},\n  note             = {This is a challenge paper.},\n  abstract         = {This paper focuses on presenting a complex real-world planning application based on a rescue mission. While temporal hierarchical planning seems to be a promising solution to such a class of problems, given its ability to consider experts' knowledge and dissect the search space, many major challenges of complex real-world planning problems are not addressed yet formally, i.e. recursive decomposition to achieve a goal state, optimization of utility functions defined for abstract tasks, and optimal allocation of tasks to multiple actors.},\n  url_paper        = {https://hierarchical-task.net/publications/hplan/2021/HPlan2021-paper12.pdf},\n  url_presentation = {https://www.youtube.com/watch?v=R9JUXsd3kC4}\n}\n\n
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\n This paper focuses on presenting a complex real-world planning application based on a rescue mission. While temporal hierarchical planning seems to be a promising solution to such a class of problems, given its ability to consider experts' knowledge and dissect the search space, many major challenges of complex real-world planning problems are not addressed yet formally, i.e. recursive decomposition to achieve a goal state, optimization of utility functions defined for abstract tasks, and optimal allocation of tasks to multiple actors.\n
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\n \n\n \n \n \n \n \n \n Towards Robust Constraint Satisfaction in Hybrid Hierarchical Planning.\n \n \n \n \n\n\n \n Tobias Schwartz; Michael Sioutis; and Diedrich Wolter.\n\n\n \n\n\n\n In Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021), pages 76–80, 2021. \n This is a challenge paper.\n\n\n\n
\n\n\n\n \n \n \"Towards paper\n  \n \n \n \"Towards presentation\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{HPlan2021paper13,\n  author           = {Tobias Schwartz and Michael Sioutis and Diedrich Wolter},\n  title            = {Towards Robust Constraint Satisfaction in Hybrid Hierarchical Planning},\n  booktitle        = {Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021)},\n  year             = {2021},\n  pages            = {76--80},\n  note             = {This is a challenge paper.},\n  abstract         = {Hybrid planning is essential for real world applications, as it allows for reasoning with different forms of abstract knowledge, such as time, space or resources. This unavoidably leads to a combinatorial explosion of the search space that has previously been tackled using a hierarchical task network (HTN) planning approach. Existing HTN planners mostly focus on finding a solution as fast as possible, with only recent work considering length-optimal solutions. In real world scenarios, it can easily happen that the environment changes before a plan is fully executed. We are motivated to conduct planning in such a way that the solution has the best chance of withstanding such changes in the environment. We call this ability the robustness of a solution. Defining robustness, however, is an inherently difficult challenge, as many different forms and notions exist. In this paper, we start the discussion by outlining a possible notion of robustness recently introduced in the light of Qualitative Spatial Reasoning (QSR) within the scope of hybrid hierarchical planning.},\n  url_paper        = {https://hierarchical-task.net/publications/hplan/2021/HPlan2021-paper13.pdf},\n  url_presentation = {https://www.youtube.com/watch?v=yTPprq1vgYs}\n}\n\n\n\n\n\n\n\n
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\n Hybrid planning is essential for real world applications, as it allows for reasoning with different forms of abstract knowledge, such as time, space or resources. This unavoidably leads to a combinatorial explosion of the search space that has previously been tackled using a hierarchical task network (HTN) planning approach. Existing HTN planners mostly focus on finding a solution as fast as possible, with only recent work considering length-optimal solutions. In real world scenarios, it can easily happen that the environment changes before a plan is fully executed. We are motivated to conduct planning in such a way that the solution has the best chance of withstanding such changes in the environment. We call this ability the robustness of a solution. Defining robustness, however, is an inherently difficult challenge, as many different forms and notions exist. In this paper, we start the discussion by outlining a possible notion of robustness recently introduced in the light of Qualitative Spatial Reasoning (QSR) within the scope of hybrid hierarchical planning.\n
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\n  \n 2020\n \n \n (5)\n \n \n
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\n \n\n \n \n \n \n \n \n Proceedings of the 3rd ICAPS Workshop on Hierarchical Planning (HPlan 2020).\n \n \n \n \n\n\n \n Pascal Bercher; Daniel Höller; Roman Barták; and Ron Alford.,\n editors.\n \n\n\n \n\n\n\n 2020.\n \n\n\n\n
\n\n\n\n \n \n \"Proceedings website\n  \n \n \n \"Proceedings proceedings\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Proceedings{HPlan2020proceedings,\n  editor          = {Pascal Bercher and Daniel H{\\"o}ller and Roman Bart{\\'a}k and Ron Alford},\n  title           = {Proceedings of the 3rd ICAPS Workshop on Hierarchical Planning (HPlan 2020)},\n  year            = {2020},\n  url_website     = {http://hplan2020.hierarchical-task.net},\n  url_proceedings = {https://hierarchical-task.net/publications/hplan/HPlanProceedings-2020.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n \n Formalising German Legal Opinions as Planning.\n \n \n \n \n\n\n \n Gregor Behnke.\n\n\n \n\n\n\n In Proceedings of the 3rd ICAPS Workshop on Hierarchical Planning (HPlan 2020), pages 1–8, 2020. \n \n\n\n\n
\n\n\n\n \n \n \"Formalising paper\n  \n \n \n \"Formalising presentation\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{HPlan2020paper1,\n  author           = {Gregor Behnke},\n  title            = {Formalising German Legal Opinions as Planning},\n  booktitle        = {Proceedings of the 3rd ICAPS Workshop on Hierarchical Planning (HPlan 2020)},\n  year             = {2020},\n  pages            = {1--8},\n  abstract         = {A legal opinion in the German legal system is a formal piece of writing that investigates whether a given statement of law is true or not given a description of a specific case. Writing these opinions is the central element of German legal education, but is supported only by basic IT technologies, such as text-based search engines. Formalising legal thoughts would enable the creation of various tools that support students, lawyers, and judges in correctly applying the law.<br/>\n  German legal opinions are interesting are interesting from a research perspective as they follow a strictly formalised structure and method of argumentation. In practice, these opinions can (often) be seen as a thorough application of so-called schemata. A schemata provides a fixed way to check whether a specific assertion of law holds or not by providing sub-assertions to check and a rule on how these results should be combined. In essence, these schemata therefore describe a hierarchical (but potentially recursive) structure on legal terms and properties.<br/>\n  We propose a formalisation of these schemata in terms of Hierarchical Task Network (HTN) planning. The modelled domain will describe the application of the law on the specifics of a given case s.t. the resulting plan and its decompositional structure will constitute the structure of a legal opinion on the case.},\n  url_paper        = {https://hierarchical-task.net/publications/hplan/2020/HPlan2020-paper1.pdf},\n  url_presentation = {https://www.youtube.com/watch?v=krgR0TpQ-9Y}\n}\n\n
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\n A legal opinion in the German legal system is a formal piece of writing that investigates whether a given statement of law is true or not given a description of a specific case. Writing these opinions is the central element of German legal education, but is supported only by basic IT technologies, such as text-based search engines. Formalising legal thoughts would enable the creation of various tools that support students, lawyers, and judges in correctly applying the law.
German legal opinions are interesting are interesting from a research perspective as they follow a strictly formalised structure and method of argumentation. In practice, these opinions can (often) be seen as a thorough application of so-called schemata. A schemata provides a fixed way to check whether a specific assertion of law holds or not by providing sub-assertions to check and a rule on how these results should be combined. In essence, these schemata therefore describe a hierarchical (but potentially recursive) structure on legal terms and properties.
We propose a formalisation of these schemata in terms of Hierarchical Task Network (HTN) planning. The modelled domain will describe the application of the law on the specifics of a given case s.t. the resulting plan and its decompositional structure will constitute the structure of a legal opinion on the case.\n
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\n \n\n \n \n \n \n \n \n Landmark Extraction in HTN Planning.\n \n \n \n \n\n\n \n Daniel Höller; and Pascal Bercher.\n\n\n \n\n\n\n In Proceedings of the 3rd ICAPS Workshop on Hierarchical Planning (HPlan 2020), pages 9–17, 2020. \n A follow-up paper was later accepted at AAAI 2021.\n\n\n\n
\n\n\n\n \n \n \"Landmark paper\n  \n \n \n \"Landmark presentation\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{HPlan2020paper2,\n  author           = {Daniel H\\"{o}ller and Pascal Bercher},\n  title            = {Landmark Extraction in HTN Planning},\n  booktitle        = {Proceedings of the 3rd ICAPS Workshop on Hierarchical Planning (HPlan 2020)},\n  year             = {2020},\n  pages            = {9--17},\n  abstract         = {Landmarks are state features that need to be made true or tasks that need to be contained in every solution of a planning problem. They are a valuable source of information in planning and can be exploited in various ways. Landmarks have been used both in classical and hierarchical planning, but while there is much work in classical planning, the techniques in hierarchical planning are less evolved. In this paper we introduce a novel landmark generation method for Hierarchical Task Network (HTN) planning and show that it is sound and incomplete. We show that every complete approach is as hard as the underlying HTN problem. Since we make relaxations during landmark generation, this means NP-hard for our setting (while our approach is in P). On a widely used benchmark set, our approach finds more than twice the number of landmarks than the approach from the literature. Though our focus is on landmark generation, we show that the newly discovered landmarks bear information beneficial for solvers.},\n  url_paper        = {https://hierarchical-task.net/publications/hplan/2020/HPlan2020-paper2.pdf},\n  note             = {A follow-up paper was later accepted at AAAI 2021.},\n  url_presentation = {https://www.youtube.com/watch?v=qtZUxEpquOc}\n}\n\n
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\n Landmarks are state features that need to be made true or tasks that need to be contained in every solution of a planning problem. They are a valuable source of information in planning and can be exploited in various ways. Landmarks have been used both in classical and hierarchical planning, but while there is much work in classical planning, the techniques in hierarchical planning are less evolved. In this paper we introduce a novel landmark generation method for Hierarchical Task Network (HTN) planning and show that it is sound and incomplete. We show that every complete approach is as hard as the underlying HTN problem. Since we make relaxations during landmark generation, this means NP-hard for our setting (while our approach is in P). On a widely used benchmark set, our approach finds more than twice the number of landmarks than the approach from the literature. Though our focus is on landmark generation, we show that the newly discovered landmarks bear information beneficial for solvers.\n
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\n \n\n \n \n \n \n \n \n Planning Using Combinatory Categorial Grammars.\n \n \n \n \n\n\n \n Christopher Geib; and Janith Weerasinghe.\n\n\n \n\n\n\n In Proceedings of the 3rd ICAPS Workshop on Hierarchical Planning (HPlan 2020), pages 18–26, 2020. \n \n\n\n\n
\n\n\n\n \n \n \"Planning paper\n  \n \n \n \"Planning presentation\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{HPlan2020paper3,\n  author           = {Christopher Geib and Janith Weerasinghe},\n  title            = {Planning Using Combinatory Categorial Grammars},\n  booktitle        = {Proceedings of the 3rd ICAPS Workshop on Hierarchical Planning (HPlan 2020)},\n  year             = {2020},\n  pages            = {18--26},\n  abstract         = {This paper presents a new model of planning based on representing domain knowledge using Combinatorial Categorial Grammars taken from natural language processing. This enables the capturing of plans with context-free expressiveness. It uses the same representation that has previously been used for plan recognition and has been shown to be learnable. Thus it represents a solid link between planning, plan recognition, and natural language processing. The paper also compares our open source implementation to two other well known hierarchical planners.},\n  url_paper        = {https://hierarchical-task.net/publications/hplan/2020/HPlan2020-paper3.pdf},\n  url_presentation = {https://www.youtube.com/watch?v=f5X515famgI}\n}\n\n
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\n This paper presents a new model of planning based on representing domain knowledge using Combinatorial Categorial Grammars taken from natural language processing. This enables the capturing of plans with context-free expressiveness. It uses the same representation that has previously been used for plan recognition and has been shown to be learnable. Thus it represents a solid link between planning, plan recognition, and natural language processing. The paper also compares our open source implementation to two other well known hierarchical planners.\n
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\n \n\n \n \n \n \n \n \n Stable Plan Repair for State-Space HTN Planning.\n \n \n \n \n\n\n \n Robert P. Goldman; Ugur Kuter; and Richard G. Freedman.\n\n\n \n\n\n\n In Proceedings of the 3rd ICAPS Workshop on Hierarchical Planning (HPlan 2020), pages 27–35, 2020. \n \n\n\n\n
\n\n\n\n \n \n \"Stable paper\n  \n \n \n \"Stable presentation\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{HPlan2020paper4,\n  author           = {Robert P. Goldman and Ugur Kuter and Richard G. Freedman},\n  title            = {Stable Plan Repair for State-Space HTN Planning},\n  booktitle        = {Proceedings of the 3rd ICAPS Workshop on Hierarchical Planning (HPlan 2020)},\n  year             = {2020},\n  pages            = {27--35},\n  abstract         = {This paper describes our approach, S HOP FIXER, to plan repair in Hierarchical Task Network (HTN) planning. We developed SHOPF IXER in the SHOP3 HTN planning framework, extending S HOP 3’s HTN language and theorem-proving capabilities in several ways. Unlike many existing HTN plan repair approaches that depend on chronological backtracking, S HOPF IXER uses backjumping techniques to efficiently, correctly and stably repair the hierarchical plans, where stability means with minimal perturbation to the original plan. We describe our new plan repair method and present experimental results in a number of IPC domains, demonstrating that it generates plans with limited perturbations, and that its plan repair is more computationally efficient than replanning. We compare our results with earlier experimental results from Fox, et al. on plan repair and plan stability. Our results confirm theirs, and generalize them. Specifically, we generalize their LPG-repair algorithm to handle plan upsets during execution, and evaluate it in such situations.},\n  url_paper        = {https://hierarchical-task.net/publications/hplan/2020/HPlan2020-paper4.pdf},\n  url_presentation = {https://www.youtube.com/watch?v=sRjJH2294kI}\n}\n\n\n\n\n\n\n\n
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\n This paper describes our approach, S HOP FIXER, to plan repair in Hierarchical Task Network (HTN) planning. We developed SHOPF IXER in the SHOP3 HTN planning framework, extending S HOP 3’s HTN language and theorem-proving capabilities in several ways. Unlike many existing HTN plan repair approaches that depend on chronological backtracking, S HOPF IXER uses backjumping techniques to efficiently, correctly and stably repair the hierarchical plans, where stability means with minimal perturbation to the original plan. We describe our new plan repair method and present experimental results in a number of IPC domains, demonstrating that it generates plans with limited perturbations, and that its plan repair is more computationally efficient than replanning. We compare our results with earlier experimental results from Fox, et al. on plan repair and plan stability. Our results confirm theirs, and generalize them. Specifically, we generalize their LPG-repair algorithm to handle plan upsets during execution, and evaluate it in such situations.\n
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\n  \n 2019\n \n \n (8)\n \n \n
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\n \n\n \n \n \n \n \n \n Proceedings of the 2nd ICAPS Workshop on Hierarchical Planning (HPlan 2019).\n \n \n \n \n\n\n \n Pascal Bercher; Gregor Behnke; Vikas Shivashankar; and Ron Alford.,\n editors.\n \n\n\n \n\n\n\n 2019.\n \n\n\n\n
\n\n\n\n \n \n \"Proceedings website\n  \n \n \n \"Proceedings proceedings\n  \n \n \n \"Proceedings openreview\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Proceedings{HPlan2019proceedings,\n  editor          = {Pascal Bercher and Gregor Behnke and Vikas Shivashankar and Ron Alford},\n  title           = {Proceedings of the 2nd ICAPS Workshop on Hierarchical Planning (HPlan 2019)},\n  year            = {2019},\n  url_website     = {http://hplan2019.hierarchical-task.net},\n  url_proceedings = {https://hierarchical-task.net/publications/hplan/HPlanProceedings-2019.pdf},\n  url_openReview  = {https://openreview.net/group?id=icaps-conference.org/ICAPS/2019/Workshop/Hierarchical_Planning#all-submissions}\n}\n\n
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\n \n\n \n \n \n \n \n \n Construction-Planning Models in Minecraft.\n \n \n \n \n\n\n \n Julia Wichlacz; Álvaro Torralba; and Jörg Hoffmann.\n\n\n \n\n\n\n In Proceedings of the 2nd ICAPS Workshop on Hierarchical Planning (HPlan 2019), pages 1–5, 2019. \n \n\n\n\n
\n\n\n\n \n \n \"Construction-Planning paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{HPlan2019paper1,\n  author    = {Julia Wichlacz and \\'{A}lvaro Torralba, and J\\"{o}rg Hoffmann},\n  title     = {Construction-Planning Models in Minecraft},\n  booktitle = {Proceedings of the 2nd ICAPS Workshop on Hierarchical Planning (HPlan 2019)},\n  year      = {2019},\n  pages     = {1--5},\n  abstract  = {Minecraft is a videogame that offers many interesting challenges for AI systems. In this paper, we focus on construction scenarios where an agent must build a complex structure made of individual blocks. As higher-level objects are formed of lower-level objects, the construction can naturally be modelled as a hierarchical task network. We model a house-construction scenario in classical and HTN planning and compare the advantages and disadvantages of both kinds of models.},\n  url_paper = {https://openreview.net/pdf?id=BkgyvHSWFV}\n}\n\n
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\n Minecraft is a videogame that offers many interesting challenges for AI systems. In this paper, we focus on construction scenarios where an agent must build a complex structure made of individual blocks. As higher-level objects are formed of lower-level objects, the construction can naturally be modelled as a hierarchical task network. We model a house-construction scenario in classical and HTN planning and compare the advantages and disadvantages of both kinds of models.\n
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\n \n\n \n \n \n \n \n \n HDDL – A Language to Describe Hierarchical Planning Problems.\n \n \n \n \n\n\n \n Daniel Höller; Gregor Behnke; Pascal Bercher; Susanne Biundo; Humbert Fiorino; Damien Pellier; and Ron Alford.\n\n\n \n\n\n\n In Proceedings of the 2nd ICAPS Workshop on Hierarchical Planning (HPlan 2019), pages 6–14, 2019. \n A follow-up paper was later accepted at AAAI 2020.\n\n\n\n
\n\n\n\n \n \n \"HDDL paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{HPlan2019paper2,\n  author    = {Daniel H\\"{o}ller and Gregor Behnke and Pascal Bercher and Susanne Biundo and Humbert Fiorino Fiorino and Damien Pellier, and Ron Alford},\n  title     = {HDDL -- A Language to Describe Hierarchical Planning Problems},\n  booktitle = {Proceedings of the 2nd ICAPS Workshop on Hierarchical Planning (HPlan 2019)},\n  year      = {2019},\n  pages     = {6--14},\n  abstract  = {The research in hierarchical planning has made considerable progress in the last few years. Many recent systems do not rely on hand-tailored advice anymore to find solutions, but are supposed to be domain-independent systems that come with sophisticated solving techniques. In principle, this development would make the comparison between systems easier (because the domains are not tailored to a single system anymore) and -- much more important – also the integration into other systems, because the modeling process is less tedious (due to the lack of advice) and there is no (or less) commitment to a certain planning system the model is created for. However, these advantages are destroyed by the lack of a common input language and feature set supported by the different systems. In this paper, we propose an extension to PDDL, the description language used in non-hierarchical planning, to the needs of hierarchical planning systems. We restrict our language to a basic feature set shared by many recent systems, give an extension of PDDL’s EBNF syntax definition, and discuss our extensions, especially with respect to planner-specific input languages from related work.},\n  url_paper = {https://openreview.net/pdf?id=HJeT8HBbt4},\n  note      = {A follow-up paper was later accepted at AAAI 2020.}\n}\n\n
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\n The research in hierarchical planning has made considerable progress in the last few years. Many recent systems do not rely on hand-tailored advice anymore to find solutions, but are supposed to be domain-independent systems that come with sophisticated solving techniques. In principle, this development would make the comparison between systems easier (because the domains are not tailored to a single system anymore) and – much more important – also the integration into other systems, because the modeling process is less tedious (due to the lack of advice) and there is no (or less) commitment to a certain planning system the model is created for. However, these advantages are destroyed by the lack of a common input language and feature set supported by the different systems. In this paper, we propose an extension to PDDL, the description language used in non-hierarchical planning, to the needs of hierarchical planning systems. We restrict our language to a basic feature set shared by many recent systems, give an extension of PDDL’s EBNF syntax definition, and discuss our extensions, especially with respect to planner-specific input languages from related work.\n
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\n \n\n \n \n \n \n \n \n HTN Planning with Semantic Attachments.\n \n \n \n \n\n\n \n Maurício Cecílio Magnaguagno; and Felipe Meneguzzi.\n\n\n \n\n\n\n In Proceedings of the 2nd ICAPS Workshop on Hierarchical Planning (HPlan 2019), pages 15–21, 2019. \n A follow-up paper was later accepted at AAAI 2020.\n\n\n\n
\n\n\n\n \n \n \"HTN paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{HPlan2019paper3,\n  author    = {Maur\\'{i}cio Cec\\'{i}lio Magnaguagno and Felipe Meneguzzi},\n  title     = {HTN Planning with Semantic Attachments},\n  booktitle = {Proceedings of the 2nd ICAPS Workshop on Hierarchical Planning (HPlan 2019)},\n  year      = {2019},\n  pages     = {15--21},\n  abstract  = {Hierarchical Task Networks (HTN) generate plans using a decomposition process guided by extra domain knowledge to guide search towards a planning task. While many HTN planners can make calls to external processes (e.g. to a simulator interface) during the decomposition process, this is a computationally expensive process, so planner implementations often use such calls in an ad-hoc way using very specialized domain knowledge to limit the number of calls. Conversely, the few classical planners that are capable of using external calls (often called semantic attachments) during planning do so in much more limited ways by generating a fixed number of ground operators at problem grounding time. In this paper we develop the notion of semantic attachments for HTN planning using semi co-routines, allowing such procedurally defined predicates to link the planning process to custom unifications outside of the planner. The resulting planner can then use such co-routines as part of its backtracking mechanism to search through parallel dimensions of the state-space (e.g. through numeric variables). We show empirically that our planner outperforms the state-of-the-art numeric planners in a number of domains using minimal extra domain knowledge.},\n  url_paper = {https://openreview.net/pdf?id=BkxAIrBZtE},\n  note      = {A follow-up paper was later accepted at AAAI 2020.}\n}\n\n
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\n Hierarchical Task Networks (HTN) generate plans using a decomposition process guided by extra domain knowledge to guide search towards a planning task. While many HTN planners can make calls to external processes (e.g. to a simulator interface) during the decomposition process, this is a computationally expensive process, so planner implementations often use such calls in an ad-hoc way using very specialized domain knowledge to limit the number of calls. Conversely, the few classical planners that are capable of using external calls (often called semantic attachments) during planning do so in much more limited ways by generating a fixed number of ground operators at problem grounding time. In this paper we develop the notion of semantic attachments for HTN planning using semi co-routines, allowing such procedurally defined predicates to link the planning process to custom unifications outside of the planner. The resulting planner can then use such co-routines as part of its backtracking mechanism to search through parallel dimensions of the state-space (e.g. through numeric variables). We show empirically that our planner outperforms the state-of-the-art numeric planners in a number of domains using minimal extra domain knowledge.\n
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\n \n\n \n \n \n \n \n \n Learning Domain Structure in HGNs for Nondeterministic Planning.\n \n \n \n \n\n\n \n Morgan Fine-Morris; and Héctor Muñoz-Avila.\n\n\n \n\n\n\n In Proceedings of the 2nd ICAPS Workshop on Hierarchical Planning (HPlan 2019), pages 22–30, 2019. \n \n\n\n\n
\n\n\n\n \n \n \"Learning paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{HPlan2019paper4,\n  author    = {Morgan Fine-Morris and H{\\'e}ctor Mu{\\~n}oz-Avila},\n  title     = {Learning Domain Structure in HGNs for Nondeterministic Planning},\n  booktitle = {Proceedings of the 2nd ICAPS Workshop on Hierarchical Planning (HPlan 2019)},\n  year      = {2019},\n  pages     = {22--30},\n  abstract  = {This paper presents preliminary ideas of our work for automated learning of Hierarchical Goal Networks in nondeterministic domains. We are currently implementing the ideas expressed in this paper.},\n  url_paper = {https://openreview.net/pdf?id=S1GALrBWtN}\n}\n\n
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\n This paper presents preliminary ideas of our work for automated learning of Hierarchical Goal Networks in nondeterministic domains. We are currently implementing the ideas expressed in this paper.\n
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\n \n\n \n \n \n \n \n \n Learning HTN Methods with Preference from HTN Planning Instances.\n \n \n \n \n\n\n \n Zhanhao Xiao; Hai Wan; Hankui Hankz Zhuo; Andreas Herzig; Laurent Perrussel; and Peilin Chen.\n\n\n \n\n\n\n In Proceedings of the 2nd ICAPS Workshop on Hierarchical Planning (HPlan 2019), pages 31–39, 2019. \n A follow-up paper was later accepted at AAAI 2020.\n\n\n\n
\n\n\n\n \n \n \"Learning paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{HPlan2019paper5,\n  author    = {Zhanhao Xiao and Hai Wan and Hankui Hankz Zhuo and Andreas Herzig and Laurent Perrussel and Peilin Chen},\n  title     = {Learning HTN Methods with Preference from HTN Planning Instances},\n  booktitle = {Proceedings of the 2nd ICAPS Workshop on Hierarchical Planning (HPlan 2019)},\n  year      = {2019},\n  pages     = {31--39},\n  abstract  = {The hierarchical task network (HTN) planning technique is used in a growing number of real-world applications. However in many domains, such as the logistics domain, as there exist thousands of cases, it is difficult and time-consuming for humans to specify all HTN methods to cover all desirable plans. This suggests that it is important to learn HTN methods to accomplish the tasks via decomposition. The traditional HTN-method learning approaches require complete executable plans and annotated tasks, which are often difficult to acquire in real-world applications. In this paper, we propose a novel framework to learn HTN methods from HTN instances with incomplete method sets and without annotated tasks. Besides, previous approaches demand total orders on the subtasks in the methods while our approach is capable of learning methods with partial orders. To reduce the number of methods learned, we consider priorities on methods and compute the minimal set of methods based on prioritized preferences. By taking experiments on three well-known planning domains, we demonstrate that our approach is effective, especially on solving new HTN problems.},\n  url_paper = {https://openreview.net/pdf?id=r1e6USBWF4},\n  note      = {A follow-up paper was later accepted at AAAI 2020.}\n}\n\n
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\n The hierarchical task network (HTN) planning technique is used in a growing number of real-world applications. However in many domains, such as the logistics domain, as there exist thousands of cases, it is difficult and time-consuming for humans to specify all HTN methods to cover all desirable plans. This suggests that it is important to learn HTN methods to accomplish the tasks via decomposition. The traditional HTN-method learning approaches require complete executable plans and annotated tasks, which are often difficult to acquire in real-world applications. In this paper, we propose a novel framework to learn HTN methods from HTN instances with incomplete method sets and without annotated tasks. Besides, previous approaches demand total orders on the subtasks in the methods while our approach is capable of learning methods with partial orders. To reduce the number of methods learned, we consider priorities on methods and compute the minimal set of methods based on prioritized preferences. By taking experiments on three well-known planning domains, we demonstrate that our approach is effective, especially on solving new HTN problems.\n
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\n \n\n \n \n \n \n \n \n More Succinct Grounding of HTN Planning Problems – Preliminary Results.\n \n \n \n \n\n\n \n Gregor Behnke; Daniel Höller; Pascal Bercher; and Susanne Biundo.\n\n\n \n\n\n\n In Proceedings of the 2nd ICAPS Workshop on Hierarchical Planning (HPlan 2019), pages 40–48, 2019. \n A follow-up paper was later accepted at AAAI 2020.\n\n\n\n
\n\n\n\n \n \n \"More paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{HPlan2019paper6,\n  author    = {Gregor Behnke and Daniel H\\"{o}ller and Pascal Bercher, and Susanne Biundo},\n  title     = {More Succinct Grounding of HTN Planning Problems -- Preliminary Results},\n  booktitle = {Proceedings of the 2nd ICAPS Workshop on Hierarchical Planning (HPlan 2019)},\n  year      = {2019},\n  pages     = {40--48},\n  abstract  = {Planning systems usually operate on grounded representations of the planning problems during search. Further, planners that use translations into other combinatorial problems also often perform their translations based on a grounded model. Planning models, however, are commonly defined in a lifted formalism. As such, one of the first preprocessing steps a planner performs is to generate a grounded representation. In this paper we present a new approach for grounding HTN planning problems that produces smaller groundings than the previously published method. We expect this decrease in size to lead to more efficient planners.},\n  url_paper = {https://openreview.net/pdf?id=H1lgDHr-FE},\n  note      = {A follow-up paper was later accepted at AAAI 2020.}\n}\n\n
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\n Planning systems usually operate on grounded representations of the planning problems during search. Further, planners that use translations into other combinatorial problems also often perform their translations based on a grounded model. Planning models, however, are commonly defined in a lifted formalism. As such, one of the first preprocessing steps a planner performs is to generate a grounded representation. In this paper we present a new approach for grounding HTN planning problems that produces smaller groundings than the previously published method. We expect this decrease in size to lead to more efficient planners.\n
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\n \n\n \n \n \n \n \n \n Parsing-based Approaches for Verification and Recognition of Hierarchical Plans.\n \n \n \n \n\n\n \n Roman Barták; Adrien Maillard; and Rafael C. Cardoso.\n\n\n \n\n\n\n In Proceedings of the 2nd ICAPS Workshop on Hierarchical Planning (HPlan 2019), pages 49–56, 2019. \n A follow-up paper was later accepted at ICTAI 2020.\n\n\n\n
\n\n\n\n \n \n \"Parsing-based paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{HPlan2019paper7,\n  author    = {Roman Bart{\\'a}k and Adrien Maillard, and Rafael C. Cardoso},\n  title     = {Parsing-based Approaches for Verification and Recognition of Hierarchical Plans},\n  booktitle = {Proceedings of the 2nd ICAPS Workshop on Hierarchical Planning (HPlan 2019)},\n  year      = {2019},\n  pages     = {49--56},\n  abstract  = {Hierarchical planning, in particular, Hierarchical Task Networks, was proposed as a method to describe plans by decomposition of tasks to sub-tasks until primitive tasks, actions, are obtained. Plan verification assumes a complete plan as input, and the objective is finding a task that decomposes to this plan. In plan recognition, a prefix of the plan is given and the objective is finding a task that decomposes to the (shortest) plan with the given prefix. This paper describes how to verify and recognize plans using a common method known from formal grammars, by parsing.},\n  url_paper = {https://openreview.net/pdf?id=HJgRIrHWt4},\n  note      = {A follow-up paper was later accepted at ICTAI 2020.}\n}\n\n\n\n\n\n\n\n
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\n Hierarchical planning, in particular, Hierarchical Task Networks, was proposed as a method to describe plans by decomposition of tasks to sub-tasks until primitive tasks, actions, are obtained. Plan verification assumes a complete plan as input, and the objective is finding a task that decomposes to this plan. In plan recognition, a prefix of the plan is given and the objective is finding a task that decomposes to the (shortest) plan with the given prefix. This paper describes how to verify and recognize plans using a common method known from formal grammars, by parsing.\n
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\n \n\n \n \n \n \n \n \n Proceedings of the 1st ICAPS Workshop on Hierarchical Planning (HPlan 2018).\n \n \n \n \n\n\n \n Pascal Bercher; Daniel Höller; Susanne Biundo; and Ron Alford.,\n editors.\n \n\n\n \n\n\n\n 2018.\n \n\n\n\n
\n\n\n\n \n \n \"Proceedings website\n  \n \n \n \"Proceedings proceedings\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Proceedings{HPlan2018proceedings,\n  editor          = {Pascal Bercher and Daniel H{\\"o}ller and Susanne Biundo and Ron Alford},\n  title           = {Proceedings of the 1st ICAPS Workshop on Hierarchical Planning (HPlan 2018)},\n  year            = {2018},\n  url_website     = {http://hplan2018.hierarchical-task.net},\n  url_proceedings = {https://hierarchical-task.net/publications/hplan/HPlanProceedings-2018.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n \n David R. Winer and Rogelio E. Cardona-Rivera.\n \n \n \n \n\n\n \n A Depth-Balanced Approach Requested.\n\n\n \n\n\n\n In Proceedings of the 1st ICAPS Workshop on Hierarchical Planning (HPlan 2018), pages 1–8, 2018. \n \n\n\n\n
\n\n\n\n \n \n \"David paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{HPlan2018paper1,\n  author    = {A Depth-Balanced Approach to Decompositional Planning for Problems where Hierarchical Depth is Requested},\n  title     = {David R. Winer and Rogelio E. Cardona-Rivera},\n  booktitle = {Proceedings of the 1st ICAPS Workshop on Hierarchical Planning (HPlan 2018)},\n  year      = {2018},\n  pages     = {1--8},\n  abstract  = {Hybrid planning with task insertion for solving classical planning problems, or decompositional planning, combines partial-order causal link planning with hierarchical task networks, where steps in the plan may represent composite (i.e., compound) actions that are decomposable into sub-steps using hierarchical knowledge. We have designed a planning algorithm that responds to a request for maximizing the hierarchical depth of plans while minimizing the plan length. In some applications, plans that adhere to hierarchical constraints are preferred over other valid plans. One of the main obstacles of this challenge is to incentivize the planner to insert composite actions while avoiding excessive search on the depth attribute. We introduce plan scoring heuristics that avoid over-discounting and under-discounting depth using a novel way to measure plan depth. We evaluate these heuristics on test problems and demonstrate that we can generate deep, low-cost solutions to planning problems while avoiding excessive search.},\n  url_paper = {https://icaps18.icaps-conference.org/fileadmin/alg/conferences/icaps18/workshops/workshop08/docs/Winer18DepthBalancedPlanning.pdf}\n}\n\n
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\n Hybrid planning with task insertion for solving classical planning problems, or decompositional planning, combines partial-order causal link planning with hierarchical task networks, where steps in the plan may represent composite (i.e., compound) actions that are decomposable into sub-steps using hierarchical knowledge. We have designed a planning algorithm that responds to a request for maximizing the hierarchical depth of plans while minimizing the plan length. In some applications, plans that adhere to hierarchical constraints are preferred over other valid plans. One of the main obstacles of this challenge is to incentivize the planner to insert composite actions while avoiding excessive search on the depth attribute. We introduce plan scoring heuristics that avoid over-discounting and under-discounting depth using a novel way to measure plan depth. We evaluate these heuristics on test problems and demonstrate that we can generate deep, low-cost solutions to planning problems while avoiding excessive search.\n
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\n \n\n \n \n \n \n \n \n Assumption-based Decentralized HTN Planning.\n \n \n \n \n\n\n \n Ugur Kuter; Robert P. Goldman; and Josh Hamell.\n\n\n \n\n\n\n In Proceedings of the 1st ICAPS Workshop on Hierarchical Planning (HPlan 2018), pages 9–16, 2018. \n \n\n\n\n
\n\n\n\n \n \n \"Assumption-based paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{HPlan2018paper2,\n  author    = {Ugur Kuter and Robert P. Goldman and Josh Hamell},\n  title     = {Assumption-based Decentralized HTN Planning},\n  booktitle = {Proceedings of the 1st ICAPS Workshop on Hierarchical Planning (HPlan 2018)},\n  year      = {2018},\n  pages     = {9--16},\n  abstract  = {This paper describes our approach to decentralized planning via Hierarchical Task Networks (HTNs), which we call Autonomy and Rationale Coordination Architecture for Decentralized Environments (Arcade). Arcade is a decentralized AI planning framework that can incorporate a number of Shop2 HTN planner instances. Each Shop2 instance may have a different HTN planning domain definition than the others in the framework. Arcade does not assume full communications among the planners. For this reason, Arcade planners must make and manage assumptions about parts of the world state that are not visible to them, including the tasks and plans of other planners. The individual planners also must operate asynchronously, and may receive new tasks, either from outside, or from other planners in Arcade.<br/>\n\n  In this paper, we describe our assumption-based planning approach and how Arcade coordinates multiple, asynchronously interacting HTN planners, using assumptions and task queues. We first present a formal framework, Assumption-based, Decentralized Total-order Simple Task Network (DTSTN) planning, based on Total-order Simple Task Network planning. This is necessary because of our use of Shop2-style task semantics, instead of goal semantics. Then we describe the Arcade framework, and how it implements the framework. Finally, we present preliminary experimental results in a simplified air operations planning domain, which shows that Arcade realizes the expected speed-up when applied to weakly coupled planning problems. We conclude with directions for future work.},\n  url_paper = {https://icaps18.icaps-conference.org/fileadmin/alg/conferences/icaps18/workshops/workshop08/docs/Kuter18DecentralizedPlanning.pdf}\n}\n\n
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\n This paper describes our approach to decentralized planning via Hierarchical Task Networks (HTNs), which we call Autonomy and Rationale Coordination Architecture for Decentralized Environments (Arcade). Arcade is a decentralized AI planning framework that can incorporate a number of Shop2 HTN planner instances. Each Shop2 instance may have a different HTN planning domain definition than the others in the framework. Arcade does not assume full communications among the planners. For this reason, Arcade planners must make and manage assumptions about parts of the world state that are not visible to them, including the tasks and plans of other planners. The individual planners also must operate asynchronously, and may receive new tasks, either from outside, or from other planners in Arcade.
In this paper, we describe our assumption-based planning approach and how Arcade coordinates multiple, asynchronously interacting HTN planners, using assumptions and task queues. We first present a formal framework, Assumption-based, Decentralized Total-order Simple Task Network (DTSTN) planning, based on Total-order Simple Task Network planning. This is necessary because of our use of Shop2-style task semantics, instead of goal semantics. Then we describe the Arcade framework, and how it implements the framework. Finally, we present preliminary experimental results in a simplified air operations planning domain, which shows that Arcade realizes the expected speed-up when applied to weakly coupled planning problems. We conclude with directions for future work.\n
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\n \n\n \n \n \n \n \n \n HEART: HiErarchical Abstraction for Real-Time Partial Order Causal Link Planning.\n \n \n \n \n\n\n \n Antoine Gréa; Laetitia Matignon; and Samir Aknine.\n\n\n \n\n\n\n In Proceedings of the 1st ICAPS Workshop on Hierarchical Planning (HPlan 2018), pages 17–25, 2018. \n \n\n\n\n
\n\n\n\n \n \n \"HEART: paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{HPlan2018paper3,\n  author    = {Antoine Gr\\'{e}a and Laetitia Matignon and Samir Aknine},\n  title     = {HEART: HiErarchical Abstraction for Real-Time Partial Order Causal Link Planning},\n  booktitle = {Proceedings of the 1st ICAPS Workshop on Hierarchical Planning (HPlan 2018)},\n  year      = {2018},\n  pages     = {17--25},\n  abstract  = {In recent years the ubiquity of artificial intelligence raised concerns among the uninitiated. The misunderstanding is further increased since most advances do not have explainable results. For automated planning, the research often targets speed, quality, or expressivity. Most existing solutions focus on one criteria while not addressing the others. However, human-related applications require a complex combination of all those criteria at different levels. We present a new method to compromise on these aspects while staying explainable. We aim to leave the range of potential applications as wide as possible but our main targets are human intent recognition and assistive robotics. The HEART planner is a real-time decompositional planner based on a hierarchical version of Partial Order Causal Link (POCL). It cyclically explores the plan space while making sure that intermediary high level plans are valid and will return them as approximate solutions when interrupted. These plans are proven to be a guarantee of solvability. This paper aims to evaluate that process and its results comparedcto classical approaches in terms of efficiency and quality.},\n  url_paper = {https://icaps18.icaps-conference.org/fileadmin/alg/conferences/icaps18/workshops/workshop08/docs/Grea18HEART.pdf}\n}\n\n
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\n In recent years the ubiquity of artificial intelligence raised concerns among the uninitiated. The misunderstanding is further increased since most advances do not have explainable results. For automated planning, the research often targets speed, quality, or expressivity. Most existing solutions focus on one criteria while not addressing the others. However, human-related applications require a complex combination of all those criteria at different levels. We present a new method to compromise on these aspects while staying explainable. We aim to leave the range of potential applications as wide as possible but our main targets are human intent recognition and assistive robotics. The HEART planner is a real-time decompositional planner based on a hierarchical version of Partial Order Causal Link (POCL). It cyclically explores the plan space while making sure that intermediary high level plans are valid and will return them as approximate solutions when interrupted. These plans are proven to be a guarantee of solvability. This paper aims to evaluate that process and its results comparedcto classical approaches in terms of efficiency and quality.\n
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\n \n\n \n \n \n \n \n \n HTN Plan Repair Using Unmodified Planning Systems.\n \n \n \n \n\n\n \n Daniel Höller; Pascal Bercher; Gregor Behnke; and Susanne Biundo.\n\n\n \n\n\n\n In Proceedings of the 1st ICAPS Workshop on Hierarchical Planning (HPlan 2018), pages 26–30, 2018. \n A follow-up paper was later accepted at KI 2020.\n\n\n\n
\n\n\n\n \n \n \"HTN paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{HPlan2018paper4,\n  author    = {Daniel H\\"{o}ller and Pascal Bercher and Gregor Behnke and Susanne Biundo},\n  title     = {HTN Plan Repair Using Unmodified Planning Systems},\n  booktitle = {Proceedings of the 1st ICAPS Workshop on Hierarchical Planning (HPlan 2018)},\n  year      = {2018},\n  pages     = {26--30},\n  abstract  = {To make planning feasible, planning models abstract from many details of the modeled system. When executing plans in the actual system, the model might be inaccurate in a critical point, and plan execution may fail. There are two options to handle this case: the previous solution can be modified to address the failure (plan repair), or the planning process can be re-started from the new situation (re-planning). In HTN planning, discarding the plan and generating a new one from the novel situation is not easily possible, because the HTN solution criteria make it necessary to take already executed actions into account. Therefore all approaches to repair plans in the literature are based on specialized algorithms. In this paper, we discuss the problem in detail and introduce a novel approach that makes it possible to use unchanged, offthe-shelf HTN planning systems to repair broken HTN plans. That way, no specialized solvers are needed.},\n  url_paper = {https://icaps18.icaps-conference.org/fileadmin/alg/conferences/icaps18/workshops/workshop08/docs/Hoeller18PlanRepair.pdf},\n  note      = {A follow-up paper was later accepted at KI 2020.}\n}\n\n\n
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\n To make planning feasible, planning models abstract from many details of the modeled system. When executing plans in the actual system, the model might be inaccurate in a critical point, and plan execution may fail. There are two options to handle this case: the previous solution can be modified to address the failure (plan repair), or the planning process can be re-started from the new situation (re-planning). In HTN planning, discarding the plan and generating a new one from the novel situation is not easily possible, because the HTN solution criteria make it necessary to take already executed actions into account. Therefore all approaches to repair plans in the literature are based on specialized algorithms. In this paper, we discuss the problem in detail and introduce a novel approach that makes it possible to use unchanged, offthe-shelf HTN planning systems to repair broken HTN plans. That way, no specialized solvers are needed.\n
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\n \n\n \n \n \n \n \n \n Programmatic Task Network Planning.\n \n \n \n \n\n\n \n Felix Mohr; Theodor Lettmann; Eyke Hüllermeier; and Marcel Wever.\n\n\n \n\n\n\n In Proceedings of the 1st ICAPS Workshop on Hierarchical Planning (HPlan 2018), pages 31–39, 2018. \n \n\n\n\n
\n\n\n\n \n \n \"Programmatic paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{HPlan2018paper5,\n  author    = {Felix Mohr and Theodor Lettmann and Eyke H\\"{u}llermeier and Marcel Wever},\n  title     = {Programmatic Task Network Planning},\n  booktitle = {Proceedings of the 1st ICAPS Workshop on Hierarchical Planning (HPlan 2018)},\n  year      = {2018},\n  pages     = {31--39},\n  abstract  = {Many planning problems benefit from extensions of classical planning formalisms and modeling techniques, or even require such extensions. Alternatives such as functional STRIPS or planning modulo theories have therefore been proposed in the past. Somewhat surprisingly, corresponding extensions are not available for hierarchical planning, despite their potential usefulness in applications like automated service composition. In this paper, we present programmatic task networks (PTN), a formalism that extends classical HTN planning in three ways. First, we allow both operations and methods to have outputs instead of only inputs. Second, formulas may contain interpreted terms, in particular interpreted predicates, which are evaluated by a theory realized in an external library. Third, PTN planning allows for a second type of tasks, called oracle tasks, which are not resolved by the planner itself but by external libraries. For the purpose of illustration and evaluation, the approach is applied to a real-world use case in the field of automated service composition.},\n  url_paper = {https://icaps18.icaps-conference.org/fileadmin/alg/conferences/icaps18/workshops/workshop08/docs/Mohr18ProgrammaticPlanning.pdf}\n}\n\n
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\n Many planning problems benefit from extensions of classical planning formalisms and modeling techniques, or even require such extensions. Alternatives such as functional STRIPS or planning modulo theories have therefore been proposed in the past. Somewhat surprisingly, corresponding extensions are not available for hierarchical planning, despite their potential usefulness in applications like automated service composition. In this paper, we present programmatic task networks (PTN), a formalism that extends classical HTN planning in three ways. First, we allow both operations and methods to have outputs instead of only inputs. Second, formulas may contain interpreted terms, in particular interpreted predicates, which are evaluated by a theory realized in an external library. Third, PTN planning allows for a second type of tasks, called oracle tasks, which are not resolved by the planner itself but by external libraries. For the purpose of illustration and evaluation, the approach is applied to a real-world use case in the field of automated service composition.\n
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\n \n\n \n \n \n \n \n \n Tracking Branches in Trees – A Propositional Encoding for Solving Partially-Ordered HTN Planning Problems.\n \n \n \n \n\n\n \n Gregor Behnke; Daniel Höller; and Susanne Biundo.\n\n\n \n\n\n\n In Proceedings of the 1st ICAPS Workshop on Hierarchical Planning (HPlan 2018), pages 40–47, 2018. \n A follow-up paper was later accepted at ICTAI 2018.\n\n\n\n
\n\n\n\n \n \n \"Tracking paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{HPlan2018paper6,\n  author    = {Gregor Behnke and Daniel H\\"{o}ller and Susanne Biundo},\n  title     = {Tracking Branches in Trees -- A Propositional Encoding for Solving Partially-Ordered HTN Planning Problems},\n  booktitle = {Proceedings of the 1st ICAPS Workshop on Hierarchical Planning (HPlan 2018)},\n  year      = {2018},\n  pages     = {40--47},\n  abstract  = {Planning via SAT has proven to be an efficient and versatile planning technique. Its declarative nature allows for an easy integration of additional constraints and can harness the progress made in the SAT community without the need to adapt the planner. However, there has been only little attention to SAT planning for hierarchical domains. To ease encoding, existing approaches for HTN planning require additional assumptions, like non-recursiveness or totally-ordered methods. Both limit the expressiveness of HTN planning severely. We propose the first propositional encodings which are able to solve general, i.e., partially-ordered, HTN planning problems, based on a previous encoding for totally-ordered problems. The empirical evaluation of our encoding shows that it outperforms existing HTN planners significantly.},\n  url_paper = {https://icaps18.icaps-conference.org/fileadmin/alg/conferences/icaps18/workshops/workshop08/docs/Behnke18HTNViaSAT.pdf},\n  note      = {A follow-up paper was later accepted at ICTAI 2018.}\n}\n\n
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\n Planning via SAT has proven to be an efficient and versatile planning technique. Its declarative nature allows for an easy integration of additional constraints and can harness the progress made in the SAT community without the need to adapt the planner. However, there has been only little attention to SAT planning for hierarchical domains. To ease encoding, existing approaches for HTN planning require additional assumptions, like non-recursiveness or totally-ordered methods. Both limit the expressiveness of HTN planning severely. We propose the first propositional encodings which are able to solve general, i.e., partially-ordered, HTN planning problems, based on a previous encoding for totally-ordered problems. The empirical evaluation of our encoding shows that it outperforms existing HTN planners significantly.\n
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\n \n\n \n \n \n \n \n \n XPLAN: Experiment Planning for Synthetic Biology.\n \n \n \n \n\n\n \n Ugur Kuter; Robert P. Goldman; Daniel Bryce; Jacob Beal; Matthew Dehaven; Christopher S. Geib; Alexander F. Plotnick; Tramy Nguyen; and Nicholas Roehner.\n\n\n \n\n\n\n In Proceedings of the 1st ICAPS Workshop on Hierarchical Planning (HPlan 2018), pages 48–52, 2018. \n \n\n\n\n
\n\n\n\n \n \n \"XPLAN: paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{HPlan2018paper7,\n  author    = {Ugur Kuter and Robert P. Goldman and Daniel Bryce and Jacob Beal and Matthew Dehaven and Christopher S. Geib and Alexander F. Plotnick and Tramy Nguyen and Nicholas Roehner},\n  title     = {{XPLAN}: Experiment Planning for Synthetic Biology},\n  booktitle = {Proceedings of the 1st ICAPS Workshop on Hierarchical Planning (HPlan 2018)},\n  year      = {2018},\n  pages     = {48--52},\n  abstract  = {We describe preliminary work on XPlan, a system for experiment planning in synthetic biology. In synthetic biology, as in other emerging fields, scientific exploration and engineering design must be interleaved, because of uncertainty about the underlying mechanisms. Through its experiment planning, XPlan provides a coordinating linchpin in DARPA’s Synergistic Discovery and Design (SD2) platform to automate scientific discovery, closing the loop between multiple machine learning analysis and biological design tools and wet labs to guide the discovery and design process. To accomplish this, XPlan combines design of experiments techniques with hierarchical planning, based on the Shop2 planner, to develop experimental plans that are directly executable in highly automated wet labs and to project experimental costs. In particular, XPlan formulates experimental designs and translates them into goals representing biological samples, then uses Shop2 to plan construction and measurement of samples using available laboratory resources. In ongoing work, we are developing probability models that will support value of information computations to optimize experimental plans.},\n  url_paper = {https://icaps18.icaps-conference.org/fileadmin/alg/conferences/icaps18/workshops/workshop08/docs/Kuter18XPlan.pdf}\n}\n
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\n We describe preliminary work on XPlan, a system for experiment planning in synthetic biology. In synthetic biology, as in other emerging fields, scientific exploration and engineering design must be interleaved, because of uncertainty about the underlying mechanisms. Through its experiment planning, XPlan provides a coordinating linchpin in DARPA’s Synergistic Discovery and Design (SD2) platform to automate scientific discovery, closing the loop between multiple machine learning analysis and biological design tools and wet labs to guide the discovery and design process. To accomplish this, XPlan combines design of experiments techniques with hierarchical planning, based on the Shop2 planner, to develop experimental plans that are directly executable in highly automated wet labs and to project experimental costs. In particular, XPlan formulates experimental designs and translates them into goals representing biological samples, then uses Shop2 to plan construction and measurement of samples using available laboratory resources. In ongoing work, we are developing probability models that will support value of information computations to optimize experimental plans.\n
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