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\n  \n 2016\n \n \n (7)\n \n \n
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\n \n\n \n \n \n \n \n \n Interfacing Belief-Desire-Intention Agent Systems with Geometric Reasoning for Robotics and Manufacturing.\n \n \n \n \n\n\n \n de Silva, L.; Meneguzzi, F.; Sanderson, D.; Chaplin, J. C.; Bakker, O. J.; Antzoulatos, N.; Ratchev, S.; Trentesaux, D.; Thomas, A.; and McFarlane, D.\n\n\n \n\n\n\n Service Orientation in Holonic and Multi-Agent Manufacturing, pages 179–188. Springer International Publishing, 2016.\n \n\n\n\n
\n\n\n\n \n \n \"ServicePaper\n  \n \n\n \n \n doi\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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@Inbook{Silva2016,\nauthor={de Silva, Lavindra\nand Meneguzzi, Felipe\nand Sanderson, David\nand Chaplin, Jack C.\nand Bakker, Otto J.\nand Antzoulatos, Nikolas\nand Ratchev, Svetan",\neditor="Borangiu, Theodor\nand Trentesaux, Damien\nand Thomas, Andr{\\'e}\nand McFarlane, Duncan},\nchapter={Interfacing Belief-Desire-Intention Agent Systems with Geometric Reasoning for Robotics and Manufacturing},\ntitle={Service Orientation in Holonic and Multi-Agent Manufacturing},\nyear={2016},\npublisher={Springer International Publishing},\npages={179--188},\nisbn={978-3-319-30337-6},\ndoi={10.1007/978-3-319-30337-6_17},\nurl={http://dx.doi.org/10.1007/978-3-319-30337-6_17}\n}\n\n
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\n \n\n \n \n \n \n \n \n Brainhack: a collaborative workshop for the open neuroscience community.\n \n \n \n \n\n\n \n Cameron Craddock, R.; S. Margulies, D.; Bellec, P.; Nolan Nichols, B.; Alcauter, S.; A. Barrios, F.; Burnod, Y.; J. Cannistraci, C.; Cohen-Adad, J.; De Leener, B.; Dery, S.; Downar, J.; Dunlop, K.; R. Franco, A.; Seligman Froehlich, C.; J. Gerber, A.; S. Ghosh, S.; J. Grabowski, T.; Hill, S.; Sólon Heinsfeld, A.; Matthew Hutchison, R.; Kundu, P.; R. Laird, A.; Liew, S.; J. Lurie, D.; G. McLaren, D.; Meneguzzi, F.; Mennes, M.; Mesmoudi, S.; O'Connor, D.; H. Pasaye, E.; Peltier, S.; Poline, J.; Prasad, G.; Fraga Pereira, R.; Quirion, P.; Rokem, A.; S. Saad, Z.; Shi, Y.; C. Strother, S.; Toro, R.; Q. Uddin, L.; D. Van Horn, J.; W. Van Meter, J.; C. Welsh, R.; and Xu, T.\n\n\n \n\n\n\n GigaScience, 5(1): 1–8. 2016.\n \n\n\n\n
\n\n\n\n \n \n \"Brainhack:Paper\n  \n \n\n \n \n doi\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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@Article{CameronCraddock2016,\nauthor="Cameron Craddock, R.\nand S. Margulies, Daniel\nand Bellec, Pierre\nand Nolan Nichols, B.\nand Alcauter, Sarael\nand A. Barrios, Fernando\nand Burnod, Yves\nand J. Cannistraci, Christopher\nand Cohen-Adad, Julien\nand De Leener, Benjamin\nand Dery, Sebastien\nand Downar, Jonathan\nand Dunlop, Katharine\nand R. Franco, Alexandre\nand Seligman Froehlich, Caroline\nand J. Gerber, Andrew\nand S. Ghosh, Satrajit\nand J. Grabowski, Thomas\nand Hill, Sean\nand S{\\'o}lon Heinsfeld, Anibal\nand Matthew Hutchison, R.\nand Kundu, Prantik\nand R. Laird, Angela\nand Liew, Sook-Lei\nand J. Lurie, Daniel\nand G. McLaren, Donald\nand Meneguzzi, Felipe\nand Mennes, Maarten\nand Mesmoudi, Salma\nand O'Connor, David\nand H. Pasaye, Erick\nand Peltier, Scott\nand Poline, Jean-Baptiste\nand Prasad, Gautam\nand Fraga Pereira, Ramon\nand Quirion, Pierre-Olivier\nand Rokem, Ariel\nand S. Saad, Ziad\nand Shi, Yonggang\nand C. Strother, Stephen\nand Toro, Roberto\nand Q. Uddin, Lucina\nand D. Van Horn, John\nand W. Van Meter, John\nand C. Welsh, Robert\nand Xu, Ting",\ntitle="Brainhack: a collaborative workshop for the open neuroscience community",\njournal="GigaScience",\nyear="2016",\nvolume="5",\nnumber="1",\npages="1--8",\nabstract="Brainhack events offer a novel workshop format with participant-generated content that caters to the rapidly growing open neuroscience community. Including components from hackathons and unconferences, as well as parallel educational sessions, Brainhack fosters novel collaborations around the interests of its attendees. Here we provide an overview of its structure, past events, and example projects. Additionally, we outline current innovations such as regional events and post-conference publications. Through introducing Brainhack to the wider neuroscience community, we hope to provide a unique conference format that promotes the features of collaborative, open science.",\nissn="2047-217X",\ndoi="10.1186/s13742-016-0121-x",\nurl="http://dx.doi.org/10.1186/s13742-016-0121-x"\n}\n\n
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\n Brainhack events offer a novel workshop format with participant-generated content that caters to the rapidly growing open neuroscience community. Including components from hackathons and unconferences, as well as parallel educational sessions, Brainhack fosters novel collaborations around the interests of its attendees. Here we provide an overview of its structure, past events, and example projects. Additionally, we outline current innovations such as regional events and post-conference publications. Through introducing Brainhack to the wider neuroscience community, we hope to provide a unique conference format that promotes the features of collaborative, open science.\n
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\n \n\n \n \n \n \n \n Reduction strategies for hierarchical multi-label classification in protein function prediction.\n \n \n \n\n\n \n Cerri, R.; Barros, R. C; de Carvalho, A. C.; and Jin, Y.\n\n\n \n\n\n\n BMC bioinformatics, 17(1): 373. 2016.\n \n\n\n\n
\n\n\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
\n 
@article{cerri2016reduction,\n  title={Reduction strategies for hierarchical multi-label classification in protein function prediction},\n  author={Cerri, Ricardo and Barros, Rodrigo C and de Carvalho, Andr{\\'e} CPLF and Jin, Yaochu},\n  journal={BMC bioinformatics},\n  volume={17},\n  number={1},\n  pages={373},\n  year={2016},\n  publisher={BioMed Central}\n}\n\n
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\n \n\n \n \n \n \n \n A Bayesian approach to norm identification.\n \n \n \n\n\n \n Cranefield, S.; Meneguzzi, F.; Oren, N.; and Savarimuthu, B. T. R.\n\n\n \n\n\n\n In Proceedings of the Twenty Second European Conference on Artificial Intelligence, pages 622 – 629, 2016. \n \n\n\n\n
\n\n\n\n \n\n \n \n doi\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
\n 
@INPROCEEDINGS{Cranefield2016,\n  author = {Stephen Cranefield and Felipe Meneguzzi and Nir Oren and Bastin T. R. Savarimuthu},\n  title = {A Bayesian approach to norm identification},\n  booktitle = {Proceedings of the Twenty Second European Conference on Artificial Intelligence},\n  year = {2016},\n  doi = {10.3233/978-1-61499-672-9-622}, \n  pages = {622 -- 629},\n}\n\n
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\n \n\n \n \n \n \n \n Landmark-based Plan Recognition.\n \n \n \n\n\n \n Pereira, R. F.; and Meneguzzi, F.\n\n\n \n\n\n\n In Proceedings of the Twenty Second European Conference on Artificial Intelligence, pages 1706 - 1707, 2016. \n \n\n\n\n
\n\n\n\n \n\n \n \n doi\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
\n 
@INPROCEEDINGS{Pereira2016,\n  author = {Ramon Fraga Pereira and Felipe Meneguzzi},\n  title = {Landmark-based Plan Recognition},\n  booktitle = {Proceedings of the Twenty Second European Conference on Artificial Intelligence},\n  year = {2016},\n  doi = {10.3233/978-1-61499-672-9-1706},\n  pages = {1706 - 1707},\n}\n\n
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\n \n\n \n \n \n \n \n \n DOVETAIL — An Abstraction for Classical Planning Using a Visual Metaphor.\n \n \n \n \n\n\n \n Magnaguagno, M.; Pereira, R.; and Meneguzzi, F.\n\n\n \n\n\n\n 2016.\n \n\n\n\n
\n\n\n\n \n \n \"DOVETAILPaper\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\n\n
\n 
@paper{FLAIRS1612966,\n\tauthor = {Mauricio Magnaguagno and Ramon Pereira and Felipe Meneguzzi},\n\ttitle = {DOVETAIL — An Abstraction for Classical Planning Using a Visual Metaphor},\n\tconference = {Florida Artificial Intelligence Research Society Conference},\n\tyear = {2016},\n\tkeywords = {Planning; Visualization; Metaphor},\n\tabstract = {While domain descriptions are often shared and manipulated through diagrams, most complex domains are still described using text-based languages. Code becomes an intermediary between the real-world and an abstract idea, and the programmer is merely a converter of diagrams into code. For automated planning this is no different. The state transition function is described in terms of a textual representation of actions and, although simple actions require little effort to define by the user, the learning process is often slow. New users have no metaphor to help them to visualize the domain description that they are working on and little information about why a planner fails due to formalization errors. In this paper, we propose a visual abstraction for both the planning domain actions and the planning process itself, to facilitate the design of classical planning domains. Using this abstraction, we expect to improve the learning curve for defining and subsequently diagnosing problems with new planning domains.},\n\n\turl = {http://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS16/paper/view/12966}\n}\n\n
\n
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\n While domain descriptions are often shared and manipulated through diagrams, most complex domains are still described using text-based languages. Code becomes an intermediary between the real-world and an abstract idea, and the programmer is merely a converter of diagrams into code. For automated planning this is no different. The state transition function is described in terms of a textual representation of actions and, although simple actions require little effort to define by the user, the learning process is often slow. New users have no metaphor to help them to visualize the domain description that they are working on and little information about why a planner fails due to formalization errors. In this paper, we propose a visual abstraction for both the planning domain actions and the planning process itself, to facilitate the design of classical planning domains. Using this abstraction, we expect to improve the learning curve for defining and subsequently diagnosing problems with new planning domains.\n
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\n \n\n \n \n \n \n \n Comparing Approaches to Subjectivity Classification: A Study on Portuguese Tweets.\n \n \n \n\n\n \n Moraes, S. M.; Santos, A. L.; Redecker, M.; Machado, R. M; and Meneguzzi, F. R\n\n\n \n\n\n\n In International Conference on Computational Processing of the Portuguese Language, pages 86–94, 2016. Springer\n \n\n\n\n
\n\n\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
\n 
@inproceedings{moraes2016comparing,\n  title={Comparing Approaches to Subjectivity Classification: A Study on Portuguese Tweets},\n  author={Moraes, Silvia MW and Santos, Andr{\\'e} LL and Redecker, Matheus and Machado, Rackel M and Meneguzzi, Felipe R},\n  booktitle={International Conference on Computational Processing of the Portuguese Language},\n  pages={86--94},\n  year={2016},\n  organization={Springer}\n}\n\n
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\n  \n 2015\n \n \n (12)\n \n \n
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\n \n\n \n \n \n \n \n \n Agent Technology for Intelligent Mobile Services and Smart Societies.\n \n \n \n \n\n\n \n Koch, F.; Meneguzzi, F.; and Lakkaraju, K.,\n editors.\n \n\n\n \n\n\n\n Springer, 2015.\n \n\n\n\n
\n\n\n\n \n \n \"AgentPaper\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
\n 
@book{Koch2015,\nyear={2015},\nisbn={978-3-662-46240-9},\nbooktitle={Agent Technology for Intelligent Mobile Services and Smart Societies},\neditor={Fernando Koch and Felipe Meneguzzi and Kiran Lakkaraju},\ntitle={Agent Technology for Intelligent Mobile Services and Smart Societies},\nurl={http://www.springer.com/computer/information+systems+and+applications/book/978-3-662-46240-9},\npublisher={Springer},\nlanguage={English}\n}\n\n
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\n \n\n \n \n \n \n \n \n \\BDI\\ reasoning with normative considerations .\n \n \n \n \n\n\n \n Meneguzzi, F.; Rodrigues, O.; Oren, N.; Vasconcelos, W. W.; and Luck, M.\n\n\n \n\n\n\n Engineering Applications of Artificial Intelligence , 43(0): 127 - 146. 2015.\n \n\n\n\n
\n\n\n\n \n \n \"\\BDI\\Paper\n  \n \n\n \n \n doi\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\n\n
\n 
@article{Meneguzzi2015,\ntitle = "\\{BDI\\} reasoning with normative considerations ",\njournal = "Engineering Applications of Artificial Intelligence ",\nvolume = "43",\nnumber = "0",\npages = "127 - 146",\nyear = "2015",\nnote = "",\nissn = "0952-1976",\ndoi = "http://dx.doi.org/10.1016/j.engappai.2015.04.011",\nurl = "http://www.sciencedirect.com/science/article/pii/S0952197615000925",\nauthor = "Felipe Meneguzzi and Odinaldo Rodrigues and Nir Oren and Wamberto W. Vasconcelos and Michael Luck",\nkeywords = "Multi-agent systems",\nkeywords = "Norms",\nkeywords = "\\{BDI\\}",\nkeywords = "Constraints",\nkeywords = "Planning ",\nabstract = "Abstract Systems of autonomous and self-interested agents interacting to achieve individual and collective goals may exhibit undesirable or unexpected behaviour if left unconstrained. Norms have been widely proposed as a means of defining and enforcing societal constraints by using the deontic concepts of obligations, permissions and prohibitions to describe what must, may and should not be done, respectively. However, recent efforts to provide norm-enabled agent architectures that guide plan choices suffer from interfering with an agent׳s reasoning process, and thus limit the agent׳s autonomy more than is required by the norms alone. In this paper we describe an extension of the Beliefs, Desires, Intentions (BDI) architecture that enables normative reasoning used to help agents choose and customise plans taking norms into account. The paper makes three significant contributions: we provide a formal framework to represent norms compactly and to manage them; we present a formal characterisation of the normative positions induced by norms of an agent׳s execution within a given time period; and finally, we put forth a mechanism for plan selection and ranking taking into consideration a set of normative restrictions. "\n}\n\n
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\n Abstract Systems of autonomous and self-interested agents interacting to achieve individual and collective goals may exhibit undesirable or unexpected behaviour if left unconstrained. Norms have been widely proposed as a means of defining and enforcing societal constraints by using the deontic concepts of obligations, permissions and prohibitions to describe what must, may and should not be done, respectively. However, recent efforts to provide norm-enabled agent architectures that guide plan choices suffer from interfering with an agent׳s reasoning process, and thus limit the agent׳s autonomy more than is required by the norms alone. In this paper we describe an extension of the Beliefs, Desires, Intentions (BDI) architecture that enables normative reasoning used to help agents choose and customise plans taking norms into account. The paper makes three significant contributions: we provide a formal framework to represent norms compactly and to manage them; we present a formal characterisation of the normative positions induced by norms of an agent׳s execution within a given time period; and finally, we put forth a mechanism for plan selection and ranking taking into consideration a set of normative restrictions. \n
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\n \n\n \n \n \n \n \n \n Planning in BDI agents: a survey of the integration of planning algorithms and agent reasoning.\n \n \n \n \n\n\n \n Meneguzzi, F.; and De Silva, L.\n\n\n \n\n\n\n The Knowledge Engineering Review, 30: 1–44. 1 2015.\n \n\n\n\n
\n\n\n\n \n \n \"PlanningPaper\n  \n \n\n \n \n doi\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
\n 
@article{Meneguzzi2015,\nauthor = {Meneguzzi,Felipe and De Silva,Lavindra},\ntitle = {Planning in BDI agents: a survey of the integration of planning algorithms and agent reasoning},\njournal = {The Knowledge Engineering Review},\nvolume = {30},\nissue = {01},\nmonth = {1},\nyear = {2015},\nissn = {1469-8005},\npages = {1--44},\nnumpages = {44},\ndoi = {10.1017/S0269888913000337},\nURL = {http://journals.cambridge.org/article_S0269888913000337},\nabstract = { ABSTRACT Agent programming languages have often avoided the use of automated (first principles or hierarchical) planners in favour of predefined plan/recipe libraries for computational efficiency reasons. This allows for very efficient agent reasoning cycles, but limits the autonomy and flexibility of the resulting agents, oftentimes with deleterious effects on the agent's performance. Planning agents can, for instance, synthesise a new plan to achieve a goal for which no predefined recipe worked, or plan to make viable the precondition of a recipe belonging to a goal being pursued. Recent work on integrating automated planning with belief-desire-intention (BDI)-style agent architectures has yielded a number of systems and programming languages that exploit the efficiency of standard BDI reasoning, as well as the flexibility of generating new recipes at runtime. In this paper, we survey these efforts and point out directions for future work. }\n}\n\n
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\n ABSTRACT Agent programming languages have often avoided the use of automated (first principles or hierarchical) planners in favour of predefined plan/recipe libraries for computational efficiency reasons. This allows for very efficient agent reasoning cycles, but limits the autonomy and flexibility of the resulting agents, oftentimes with deleterious effects on the agent's performance. Planning agents can, for instance, synthesise a new plan to achieve a goal for which no predefined recipe worked, or plan to make viable the precondition of a recipe belonging to a goal being pursued. Recent work on integrating automated planning with belief-desire-intention (BDI)-style agent architectures has yielded a number of systems and programming languages that exploit the efficiency of standard BDI reasoning, as well as the flexibility of generating new recipes at runtime. In this paper, we survey these efforts and point out directions for future work. \n
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\n \n\n \n \n \n \n \n \n Monitoring compliance with E-contracts and norms.\n \n \n \n \n\n\n \n Modgil, S.; Oren, N.; Faci, N.; Meneguzzi, F.; Miles, S.; and Luck, M.\n\n\n \n\n\n\n Artificial Intelligence and Law,1-36. 2015.\n \n\n\n\n
\n\n\n\n \n \n \"MonitoringPaper\n  \n \n\n \n \n doi\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\n\n
\n 
@article{Modgil2015,\nyear={2015},\nissn={0924-8463},\njournal={Artificial Intelligence and Law},\ndoi={10.1007/s10506-015-9167-9},\ntitle={Monitoring compliance with E-contracts and norms},\nurl={http://dx.doi.org/10.1007/s10506-015-9167-9},\npublisher={Springer Netherlands},\nkeywords={E-contracts; Norms; Monitoring; Multiagent systems},\nauthor={Modgil, Sanjay and Oren, Nir and Faci, Noura and Meneguzzi, Felipe and Miles, Simon and Luck, Michael},\npages={1-36},\nlanguage={English}\n}\n\n
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\n \n\n \n \n \n \n \n \n Towards Planning Uncertain Commitment Protocols.\n \n \n \n \n\n\n \n Meneguzzi, F.; Telang, P.; and Yorke-Smith, N.\n\n\n \n\n\n\n In Proceedings of the Thirteenth International Conference on Autonomous Agents and Multiagent Systems, pages 1681–1682, 2015. \n \n\n\n\n
\n\n\n\n \n \n \"TowardsPaper\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
\n 
@INPROCEEDINGS{Meneguzzi2015,\n  author = {Felipe Meneguzzi and Pankaj Telang and Neil Yorke-Smith},\n  title = {Towards Planning Uncertain Commitment Protocols},\n  booktitle = {Proceedings of the Thirteenth International Conference on Autonomous\n\tAgents and Multiagent Systems},\n  year = {2015},\n  pages = {1681--1682},\nabstract = {In the context of a business process modeled by commitments, agents enact a protocol by carrying out goals that service their part of commitments. \nIn a competitive or even in a cooperative setting, an agent does not know for sure that its partners will successfully act on their part of the commitments. \nWe introduce uncertainty into a successful recent approach of planning first-order commitment protocols. \nProbabilities reflect a semantics of the belief of an agent about the successful completion of tasks by other agents within the protocol, capturing notions of trust. \nWe take a deterministic Hierarchical Task Network (HTN) planner, introduce probabilities into the task networks, and derive a protocol enactment which maximizes expected utility from the point of view of one agent.  We illustrate our approach on a business scenario in e-commerce.},\n  file = {:http\\://www.meneguzzi.eu/felipe/pubs/aamas-commitments-probabilities-2015.pdf:PDF},\n  url = {http://www.meneguzzi.eu/felipe/pubs/aamas-commitments-probabilities-2015.pdf},\n  owner = {meneguzzi},\n  timestamp = {2015.05.29}\n}\n\n
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\n In the context of a business process modeled by commitments, agents enact a protocol by carrying out goals that service their part of commitments. In a competitive or even in a cooperative setting, an agent does not know for sure that its partners will successfully act on their part of the commitments. We introduce uncertainty into a successful recent approach of planning first-order commitment protocols. Probabilities reflect a semantics of the belief of an agent about the successful completion of tasks by other agents within the protocol, capturing notions of trust. We take a deterministic Hierarchical Task Network (HTN) planner, introduce probabilities into the task networks, and derive a protocol enactment which maximizes expected utility from the point of view of one agent. We illustrate our approach on a business scenario in e-commerce.\n
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\n \n\n \n \n \n \n \n \n A Bayesian Approach to Norm Identification.\n \n \n \n \n\n\n \n Cranefield, S.; Meneguzzi, F.; Oren, N.; and Savarimuthu, T.\n\n\n \n\n\n\n In Proceedings of the Thirteenth International Conference on Autonomous Agents and Multiagent Systems, pages 1743–1744, 2015. \n \n\n\n\n
\n\n\n\n \n \n \"APaper\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
\n 
@INPROCEEDINGS{Cranefield2015,\n  author = {Stephen Cranefield and Felipe Meneguzzi and Nir Oren and Tony Savarimuthu},\n  title = {A Bayesian Approach to Norm Identification},\n  booktitle = {Proceedings of the Thirteenth International Conference on Autonomous\n\tAgents and Multiagent Systems},\n  year = {2015},\n  pages = {1743--1744},\nabstract = {When entering a system, an agent should be aware of the obligations and prohibitions (collectively norms) that will affect it.  Several solutions to this norm identification problem have been proposed, which make use of observations of either other's norm compliant, or norm violating, behaviour. These solutions fail in situations where norms are typically violated, or complied with, respectively. In this paper we propose a Bayesian approach to norm identification which operates by learning from both norm compliant and norm violating behaviour. By utilising both types of behaviour, our work not only overcomes a major limitation of existing approaches, but also yields improved performance over the state-of-the-art. We evaluate its effectiveness empirically, showing, under certain conditions, high accuracy scores.},\n  file = {:http\\://www.meneguzzi.eu/felipe/pubs/aamas-norm-recognition-2015.pdf:PDF},\n  url = {http://www.meneguzzi.eu/felipe/pubs/aamas-norm-recognition-2015.pdf},\n  owner = {meneguzzi},\n  timestamp = {2015.05.29}\n}\n\n
\n
\n\n\n
\n When entering a system, an agent should be aware of the obligations and prohibitions (collectively norms) that will affect it. Several solutions to this norm identification problem have been proposed, which make use of observations of either other's norm compliant, or norm violating, behaviour. These solutions fail in situations where norms are typically violated, or complied with, respectively. In this paper we propose a Bayesian approach to norm identification which operates by learning from both norm compliant and norm violating behaviour. By utilising both types of behaviour, our work not only overcomes a major limitation of existing approaches, but also yields improved performance over the state-of-the-art. We evaluate its effectiveness empirically, showing, under certain conditions, high accuracy scores.\n
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\n \n\n \n \n \n \n \n \n Simulating Normative Behaviour in Multi-Agent Environments using Monitoring Artefacts.\n \n \n \n \n\n\n \n Chang, S.; and Meneguzzi, F.\n\n\n \n\n\n\n In 17th International Workshop on Coordination, Organizations, Institutions, and Norms, pages 1-16, 2015. \n \n\n\n\n
\n\n\n\n \n \n \"SimulatingPaper\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
\n 
@INPROCEEDINGS{Chang2015,\n  author = {Stephan Chang and Felipe Meneguzzi},\n  title = {Simulating Normative Behaviour in Multi-Agent Environments using Monitoring Artefacts},\n  booktitle = {17th International Workshop on Coordination, Organizations, Institutions, and Norms},\n  year = {2015},\n  pages = {1-16},\n  abstract = {While there are tools for programming Multi-Agent Systems, few provide an explicit mechanism for simulating norm-based behaviour using a variety of normative representations. \nIn this paper, we develop an artefact-based mechanism for norm processing, monitoring and enforcement and show its implementation as a framework built with CARTAGO. \nOur framework is then empirically demonstrated using a variety of enforcement settings.},\n  file = {:http\\://www.meneguzzi.eu/felipe/pubs/coin-simulating-norms-2015.pdf:PDF},\n  owner = {meneguzzi},\n  timestamp = {2015.07.28},\n  url = {http://www.meneguzzi.eu/felipe/pubs/coin-simulating-norms-2015.pdf}\n}\n\n
\n
\n\n\n
\n While there are tools for programming Multi-Agent Systems, few provide an explicit mechanism for simulating norm-based behaviour using a variety of normative representations. In this paper, we develop an artefact-based mechanism for norm processing, monitoring and enforcement and show its implementation as a framework built with CARTAGO. Our framework is then empirically demonstrated using a variety of enforcement settings.\n
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\n \n\n \n \n \n \n \n \n On the Design of Symbolic-Geometric Online Planning Systems.\n \n \n \n \n\n\n \n de Silva, L.; and Meneguzzi, F.\n\n\n \n\n\n\n In 2015 Workshop on Hybrid Reasoning (HR 2015), pages 1-8, 2015. \n \n\n\n\n
\n\n\n\n \n \n \"OnPaper\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
\n 
@INPROCEEDINGS{Li2015,\n  author = {de Silva, Lavindra and Felipe Meneguzzi},\n  title = {On the Design of Symbolic-Geometric Online Planning Systems},\n  booktitle = {2015 Workshop on Hybrid Reasoning (HR 2015)},\n  year = {2015},\n  pages = {1-8},\n  abstract = {We describe an abstract multilayered architecture for the organisation of robotic systems that takes \ninto account some of the key functionalities of existing robotic hardware and software in the literature. We demonstrate a concrete instance of the architecture by combining the popular AgentSpeak agent/robot programming language with standard motion planning algorithms. Our work offers some first insights into developing a more formal agent architecture for programming autonomous robots.},\n  file = {:http\\://www.meneguzzi.eu/felipe/pubs/hr-symbolic-geometric-2015.pdf:PDF},\n  owner = {meneguzzi},\n  timestamp = {2015.07.28},\n  url = {http://www.meneguzzi.eu/felipe/pubs/hr-symbolic-geometric-2015.pdf}\n}\n\n
\n
\n\n\n
\n We describe an abstract multilayered architecture for the organisation of robotic systems that takes into account some of the key functionalities of existing robotic hardware and software in the literature. We demonstrate a concrete instance of the architecture by combining the popular AgentSpeak agent/robot programming language with standard motion planning algorithms. Our work offers some first insights into developing a more formal agent architecture for programming autonomous robots.\n
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\n \n\n \n \n \n \n \n \n Identifying Potential Conflicts between Norms in Contracts.\n \n \n \n \n\n\n \n Aires, J. P.; de Lima, V. L. S.; and Meneguzzi, F.\n\n\n \n\n\n\n In 18th International Workshop on Coordination, Organizations, Institutions, and Norms, pages 1-6, 2015. \n \n\n\n\n
\n\n\n\n \n \n \"IdentifyingPaper\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
\n 
@INPROCEEDINGS{Aires2015,\n  author = {João Paulo Aires and Vera Lúcia Strube de Lima and Felipe Meneguzzi},\n  title = {Identifying Potential Conflicts between Norms in Contracts},\n  booktitle = {18th International Workshop on Coordination, Organizations, Institutions, and Norms},\n  year = {2015},\n  pages = {1-6},\n  abstract = {Contracts formally represent agreements between parties and often involve the exchange of goods or services. \n        In contracts, clauses define the behavior expected from parties in terms of deontic statements such as obligation, permission and prohibition. \n        These normative clauses may contain conflicting deontic statements referring to the same party in the same context, producing inconsistencies in the normative structure of the contract.\n        Our main contribution is an approach to detect potential conflicts between norms within contracts written in natural language.\n        We use a rule-based approach and natural language processing, which result in promising empirical results.\n        This constitutes a first step into automated processing of contracts in natural language.},\n  file = {:http\\://www.meneguzzi.eu/felipe/pubs/coin-nlp-norm-conflicts-2015.pdf:PDF},\n  owner = {meneguzzi},\n  timestamp = {2015.07.28},\n  url = {http://www.meneguzzi.eu/felipe/pubs/coin-nlp-norm-conflicts-2015.pdf}\n}\n\n
\n
\n\n\n
\n Contracts formally represent agreements between parties and often involve the exchange of goods or services. In contracts, clauses define the behavior expected from parties in terms of deontic statements such as obligation, permission and prohibition. These normative clauses may contain conflicting deontic statements referring to the same party in the same context, producing inconsistencies in the normative structure of the contract. Our main contribution is an approach to detect potential conflicts between norms within contracts written in natural language. We use a rule-based approach and natural language processing, which result in promising empirical results. This constitutes a first step into automated processing of contracts in natural language.\n
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\n \n\n \n \n \n \n \n \n Reinforcement Learning of Normative Monitoring Intensities.\n \n \n \n \n\n\n \n Li, J.; Meneguzzi, F.; Fagundes, M. S.; and Logan, B.\n\n\n \n\n\n\n In 18th International Workshop on Coordination, Organizations, Institutions, and Norms, pages 1-15, 2015. \n \n\n\n\n
\n\n\n\n \n \n \"ReinforcementPaper\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
\n 
@INPROCEEDINGS{Li2015,\n  author = {Jiaqi Li and Felipe Meneguzzi and Moser Silva Fagundes and Brian Logan},\n  title = {Reinforcement Learning of Normative Monitoring Intensities},\n  booktitle = {18th International Workshop on Coordination, Organizations, Institutions, and Norms},\n  year = {2015},\n  pages = {1-15},\n  abstract = {Choosing actions within norm-regulated environments involves balancing achieving one's goals and coping with any penalties for non-compliant behaviour. This choice becomes more complicated in environments where there is uncertainty. In this paper, we address the question of choosing actions in environments where there is uncertainty regarding both the outcomes of agent actions and the intensity of monitoring for norm violations. Our technique assumes no prior knowledge of probabilities over action outcomes or the likelihood of norm violations being detected by employing reinforcement learning to discover both the dynamics of the environment and the effectiveness of the enforcer. Results indicate agents become aware of greater rewards for violations when enforcement is lax, which gradually become less attractive as the enforcement is increased.},\n  file = {:http\\://www.meneguzzi.eu/felipe/pubs/coin-nmdp-asym-2015.pdf:PDF},\n  owner = {meneguzzi},\n  timestamp = {2015.07.28},\n  url = {http://www.meneguzzi.eu/felipe/pubs/coin-nmdp-asym-2015.pdf}\n}\n\n
\n
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\n Choosing actions within norm-regulated environments involves balancing achieving one's goals and coping with any penalties for non-compliant behaviour. This choice becomes more complicated in environments where there is uncertainty. In this paper, we address the question of choosing actions in environments where there is uncertainty regarding both the outcomes of agent actions and the intensity of monitoring for norm violations. Our technique assumes no prior knowledge of probabilities over action outcomes or the likelihood of norm violations being detected by employing reinforcement learning to discover both the dynamics of the environment and the effectiveness of the enforcer. Results indicate agents become aware of greater rewards for violations when enforcement is lax, which gradually become less attractive as the enforcement is increased.\n
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\n \n\n \n \n \n \n \n Distributed Fault Diagnostic for Multiple Mobile Robots Using an Agent Programming Language.\n \n \n \n\n\n \n \n\n\n \n\n\n\n In Proceedings of the 17th International Conference on Advanced Robotics, pages 395-400, 2015. \n \n\n\n\n
\n\n\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\n\n
\n 
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\n  Programming autonomous multi-robot systems can be extremely complex without the use of appropriate software development techniques to abstract the hardware heterogeneity from the complexity of distributed software to coordinate autonomous behavior. Moreover, real environments are dynamic, which can generate unpredictable events that can lead the robots to failure. This paper presents a highly abstract cooperative fault diagnostic method for a team of mobile robots described on a programming environment based on ROS (Robot Operating System) and the Jason multi-agent framework. When a robot detects a failure, it can perform two types of diagnostic methods: a local method executed on the faulty robot itself and a cooperative method where another robot helps the faulty robot to determine the source of failure. A case study demonstrates the success of the approach on two turtlebots.\n
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\n  \n\n  \n  \n  \n  \n  \n  A Portable OpenCL-Based Approach for SVMs in GPU.\n  \n  \n  \n\n\n  \n  Cagnin, H. E.; Winck, A. T;  and Barros, R. C\n\n\n  \n\n\n\n  In 2015 Brazilian Conference on Intelligent Systems (BRACIS), pages 198–203,  2015. IEEE\n  \n\n\n\n
\n\n\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
\n  
@inproceedings{cagnin2015portable,\n  title={A Portable OpenCL-Based Approach for SVMs in GPU},\n  author={Cagnin, Henry EL and Winck, Ana T and Barros, Rodrigo C},\n  booktitle={2015 Brazilian Conference on Intelligent Systems (BRACIS)},\n  pages={198--203},\n  year={2015},\n  organization={IEEE}\n}\n
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\n       \n      2014\n      \n        \n        (6)\n        \n      \n    
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\n  \n\n  \n  \n  \n  \n  \n  \n    Probabilistic Plan Recognition for Proactive Assistant Agents.\n  \n  \n  \n  \n\n\n  \n  Jean Oh, F. M.;  and Sycara, K.\n\n\n  \n\n\n\n  In Sukthankar, G.; Goldman, R. P.; Geib, C.; Pynadath, D. V.;  and Bui, H. H., editor(s), Plan, Activity, and Intent Recognition: Theory and Practice, pages 275-288. Elsevier,  2014.\n  \n\n\n\n
\n\n\n\n  \n  \n    \"ProbabilisticPaper\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
\n  
@incollection{Oh2014,\nyear={2014},\nisbn={978-0123985323},\nbooktitle={Plan, Activity, and Intent Recognition: Theory and Practice},\neditor={Gita Sukthankar and Robert P. Goldman and Christopher Geib and David V. Pynadath and Hung  Hai Bui},\ntitle={Probabilistic Plan Recognition for Proactive Assistant Agents},\nurl={http://store.elsevier.com/Plan-Activity-and-Intent-Recognition/isbn-9780123985323/},\npublisher={Elsevier},\nauthor={Jean Oh, Felipe Meneguzzi and Katia Sycara},\npages={275-288},\nlanguage={English}\n}\n\n\n
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\n  \n\n  \n  \n  \n  \n  \n  \n    Norm Monitoring with Asymmetric Information.\n  \n  \n  \n  \n\n\n  \n  Meneguzzi, F.; Logan, B.;  and Fagundes, M.\n\n\n  \n\n\n\n  In Proceedings of the Thirteenth International Conference on Autonomous Agents and Multiagent Systems, pages 1523–1524,  2014. \n  \n\n\n\n
\n\n\n\n  \n  \n    \"NormPaper\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
\n  
@INPROCEEDINGS{Meneguzzi2014,\n  author = {Felipe Meneguzzi and Brian Logan and Moser Fagundes},\n  title = {Norm Monitoring with Asymmetric Information},\n  booktitle = {Proceedings of the Thirteenth International Conference on Autonomous\n\tAgents and Multiagent Systems},\n  year = {2014},\n  pages = {1523--1524},\nabstract = {In this paper we consider the implications of imperfect monitoring in a stochastic environment for both the agents and the normative organisation in a normative MAS. We introduce a notion of information asymmetry to characterise the agents’ knowledge of the monitoring strategy, and show that there are potential benefits of information asymmetry for the normative organisation in reducing its cost of enforcement.},\n  file = {:http\\://www.meneguzzi.eu/felipe/pubs/aamas-nmdp-asym-2014.pdf:PDF},\n  url = {http://www.meneguzzi.eu/felipe/pubs/aamas-nmdp-asym-2014.pdf},\n  owner = {meneguzzi},\n  timestamp = {2014.05.30}\n}\n\n
\n
\n\n\n
\n  In this paper we consider the implications of imperfect monitoring in a stochastic environment for both the agents and the normative organisation in a normative MAS. We introduce a notion of information asymmetry to characterise the agents’ knowledge of the monitoring strategy, and show that there are potential benefits of information asymmetry for the normative organisation in reducing its cost of enforcement.\n
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\n  \n\n  \n  \n  \n  \n  \n  \n    Dealing with Ambiguity in Plan Recognition under Time Constraints.\n  \n  \n  \n  \n\n\n  \n  Fagundes, M.; Meneguzzi, F.; Bordini, R. H.;  and Vieira, R.\n\n\n  \n\n\n\n  In Proceedings of the Thirteenth International Conference on Autonomous Agents and Multiagent Systems, pages 389–396,  2014. \n  \n\n\n\n
\n\n\n\n  \n  \n    \"DealingPaper\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
\n  
@INPROCEEDINGS{Fagundes2014,\n  author = {Moser Fagundes and Felipe Meneguzzi and Rafael H. Bordini and Renata Vieira},\n  title = {Dealing with Ambiguity in Plan Recognition under Time Constraints},\n  booktitle = {Proceedings of the Thirteenth International Conference on Autonomous\n\tAgents and Multiagent Systems},\n  year = {2014},\n  pages = {389--396},\nabstract = {Plan recognition has been widely used in agents that need to infer which plans are being executed or which activities are being performed by others. In many applications reasoning and acting in response to plan recognition requires time. In such systems, plan recognition is expected to be made not only with precision, but also in a timely fashion. When recognition cannot be made in time, the plan recognition agent can interact with the observed agents to disambiguate multiple hypotheses, however, such an intrusive behavior is either not possible, very costly, or undesirable. In this paper, we focus on the problem of deciding when to interact with the observed agents in order to determine their plans under execution. To tackle this problem, we develop a plan recognizer that, on the one hand is the least intrusive possible, and on the other hand, attempts to recognize the plans of the observed agents with precision as soon as possible and no later than it is viable to respond to the recognized plan.},\n  file = {:http\\://www.meneguzzi.eu/felipe/pubs/aamas-intention-rec-2014.pdf:PDF},\n  url = {http://www.meneguzzi.eu/felipe/pubs/aamas-intention-rec-2014.pdf},\n  owner = {meneguzzi},\n  timestamp = {2014.05.31}\n}\n\n
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\n\n\n
\n  Plan recognition has been widely used in agents that need to infer which plans are being executed or which activities are being performed by others. In many applications reasoning and acting in response to plan recognition requires time. In such systems, plan recognition is expected to be made not only with precision, but also in a timely fashion. When recognition cannot be made in time, the plan recognition agent can interact with the observed agents to disambiguate multiple hypotheses, however, such an intrusive behavior is either not possible, very costly, or undesirable. In this paper, we focus on the problem of deciding when to interact with the observed agents in order to determine their plans under execution. To tackle this problem, we develop a plan recognizer that, on the one hand is the least intrusive possible, and on the other hand, attempts to recognize the plans of the observed agents with precision as soon as possible and no later than it is viable to respond to the recognized plan.\n
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\n  \n\n  \n  \n  \n  \n  \n  Analyzing the tradeoff between efficiency and cost of norm enforcement in stochastic environments populated with self-interested agents.\n  \n  \n  \n\n\n  \n  Fagundes, M. S.; Ossowski, S.;  and Meneguzzi, F.\n\n\n  \n\n\n\n  In Proceedings of the Twenty First European Conference on Artificial Intelligence, pages 1003-1004,  2014. \n  \n\n\n\n
\n\n\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
\n  
@INPROCEEDINGS{Fagundes2014,\n  author = {Moser Silva Fagundes and Sascha Ossowski and Felipe Meneguzzi},\n  title = {Analyzing the tradeoff between efficiency and cost of norm enforcement in stochastic environments populated with self-interested agents},\n  booktitle = {Proceedings of the Twenty First European Conference on Artificial Intelligence},\n  year = {2014},\n  pages = {1003-1004},\n}\n\n
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\n  \n\n  \n  \n  \n  \n  \n  \n    Utilizing Permission Norms in BDI Practical Normative Reasoning.\n  \n  \n  \n  \n\n\n  \n  Alrawagfeh, W.;  and Meneguzzi, F.\n\n\n  \n\n\n\n  In 16th International Workshop on Coordination, Organizations, Institutions, and Norms,  2014. \n  \n\n\n\n
\n\n\n\n  \n  \n    \"UtilizingPaper\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
\n  
@INPROCEEDINGS{Alrawagfeh2014,\n  author = {Wagdi Alrawagfeh and Felipe Meneguzzi},\n  title = {Utilizing Permission Norms in BDI Practical Normative Reasoning},\n  booktitle = {16th International Workshop on Coordination, Organizations, Institutions, and Norms},\n  year = {2014},\n  abstract = {Norms have been used in multiagent systems as a standard description of agents’ behaviors. A lot of effort has been put in formalizing norms and utilizing them in agents’ decision making. Most of this work focuses on two types of norms: prohibitions and obligations. Agents may have incomplete knowledge about norms in a system for several reasons, such as, deficient norms identification techniques or because norms are not fixed and they may change and emerge, etc. In this work we argue that, by assuming that agents do not have complete knowledge of the norms within a system permission norms are fundamental for modeling unknown normative states. Using Event Calculus, we propose a formal representation of permission norm and we show how to use it in agent normative practical reasoning. A simple mineral mining scenario has been used to demonstrate our work, which was implemented in a popular agent programming language.},\n  file = {:http\\://www.meneguzzi.eu/felipe/pubs/coin-permissions-2014.pdf:PDF},\n  owner = {meneguzzi},\n  timestamp = {2014.05.31},\n  url = {http://www.meneguzzi.eu/felipe/pubs/coin-permissions-2014.pdf}\n}\n\n
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\n\n\n
\n  Norms have been used in multiagent systems as a standard description of agents’ behaviors. A lot of effort has been put in formalizing norms and utilizing them in agents’ decision making. Most of this work focuses on two types of norms: prohibitions and obligations. Agents may have incomplete knowledge about norms in a system for several reasons, such as, deficient norms identification techniques or because norms are not fixed and they may change and emerge, etc. In this work we argue that, by assuming that agents do not have complete knowledge of the norms within a system permission norms are fundamental for modeling unknown normative states. Using Event Calculus, we propose a formal representation of permission norm and we show how to use it in agent normative practical reasoning. A simple mineral mining scenario has been used to demonstrate our work, which was implemented in a popular agent programming language.\n
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\n  \n\n  \n  \n  \n  \n  \n  Imperfect norm enforcement in stochastic environments: an analysis of efficiency and cost tradeoffs.\n  \n  \n  \n\n\n  \n  Fagundes, M. S.; Ossowski, S.;  and Meneguzzi, F.\n\n\n  \n\n\n\n  In Ibero-American Conference on Artificial Intelligence, pages 523–535,  2014. Springer\n  \n\n\n\n
\n\n\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
\n  
@inproceedings{fagundes2014imperfect,\n  title={Imperfect norm enforcement in stochastic environments: an analysis of efficiency and cost tradeoffs},\n  author={Fagundes, Moser Silva and Ossowski, Sascha and Meneguzzi, Felipe},\n  booktitle={Ibero-American Conference on Artificial Intelligence},\n  pages={523--535},\n  year={2014},\n  organization={Springer}\n}\n\n
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\n       \n      2013\n      \n        \n        (5)\n        \n      \n    
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\n  \n\n  \n  \n  \n  \n  \n  \n    Normative Agents.\n  \n  \n  \n  \n\n\n  \n  Luck, M.; Mahmoud, S.; Meneguzzi, F.; Kollingbaum, M.; Norman, T. J.; Criado, N.;  and Fagundes, M. S.\n\n\n  \n\n\n\n  In Ossowski, S., editor(s), Agreement Technologies, volume 8, of Law, Governance and Technology Series, pages 209-220. Springer Netherlands,  2013.\n  \n\n\n\n
\n\n\n\n  \n  \n    \"NormativePaper\n   \n  \n\n  \n  \n    doi\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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@incollection{Luck2013,\nyear={2013},\nisbn={978-94-007-5582-6},\nbooktitle={Agreement Technologies},\nvolume={8},\nseries={Law, Governance and Technology Series},\neditor={Ossowski, Sascha},\ndoi={10.1007/978-94-007-5583-3_14},\ntitle={Normative Agents},\nurl={http://dx.doi.org/10.1007/978-94-007-5583-3_14},\npublisher={Springer Netherlands},\nauthor={Luck, Michael and Mahmoud, Samhar and Meneguzzi, Felipe and Kollingbaum, Martin and Norman, Timothy J. and Criado, Natalia and Fagundes, Moser Silva},\npages={209-220},\nlanguage={English}\n}\n\n
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\n  \n\n  \n  \n  \n  \n  \n  \n    Prognostic normative reasoning.\n  \n  \n  \n  \n\n\n  \n  Oh, J.; Meneguzzi, F.; Sycara, K.;  and Norman, T. J.\n\n\n  \n\n\n\n  Engineering Applications of Artificial Intelligence, 26(2): 863 – 872.  2013.\n  \n\n\n\n
\n\n\n\n  \n  \n    \"PrognosticPaper\n   \n  \n\n  \n  \n    doi\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\n\n
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@article{Oh2013,\ntitle = {Prognostic normative reasoning},\njournal = {Engineering Applications of Artificial Intelligence},\nvolume = {26},\nnumber = {2},\npages = {863 -- 872},\nyear = {2013},\nissn = {0952-1976},\ndoi = {10.1016/j.engappai.2012.12.006},\nurl = {http://www.sciencedirect.com/science/article/pii/S0952197612003144},\nauthor = {Jean Oh and Felipe Meneguzzi and Katia Sycara and Timothy J. Norman},\nkeywords = {Proactive agents},\nkeywords = {Normative reasoning},\nkeywords = {Agent architecture},\nabstract = {Human users planning for multiple objectives in complex environments are subjected to high levels of cognitive workload, which can severely impair the quality of the plans created. This paper describes a software agent that can proactively assist cognitively overloaded users by providing normative reasoning about prohibitions and obligations so that the user can focus on her primary objectives. In order to provide proactive assistance, we develop the notion of prognostic normative reasoning (PNR) that consists of the following steps: (1) recognizing the user's planned activities, (2) reasoning about norms to evaluate those predicted activities, and (3) providing necessary assistance so that the user's activities are consistent with norms. The idea of PNR integrates various AI techniques, namely, user intention recognition, normative reasoning over a user's intention, and planning, execution and replanning for assistive actions. In this paper, we describe an agent architecture for PNR and discuss practical applications.}\n}\n\n
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\n  Human users planning for multiple objectives in complex environments are subjected to high levels of cognitive workload, which can severely impair the quality of the plans created. This paper describes a software agent that can proactively assist cognitively overloaded users by providing normative reasoning about prohibitions and obligations so that the user can focus on her primary objectives. In order to provide proactive assistance, we develop the notion of prognostic normative reasoning (PNR) that consists of the following steps: (1) recognizing the user's planned activities, (2) reasoning about norms to evaluate those predicted activities, and (3) providing necessary assistance so that the user's activities are consistent with norms. The idea of PNR integrates various AI techniques, namely, user intention recognition, normative reasoning over a user's intention, and planning, execution and replanning for assistive actions. In this paper, we describe an agent architecture for PNR and discuss practical applications.\n
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\n  \n\n  \n  \n  \n  \n  \n  \n    A First-Order Formalization of Commitments and Goals for Planning.\n  \n  \n  \n  \n\n\n  \n  Meneguzzi, F.; Telang, P. R.;  and Singh, M. P.\n\n\n  \n\n\n\n  In AAAI, pages 697–703,  2013. \n  \n\n\n\n
\n\n\n\n  \n  \n    \"ALink\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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@inproceedings{DBLP:conf/aaai/MeneguzziTS13,\n  author    = {Felipe Meneguzzi and\n               Pankaj R. Telang and\n               Munindar P. Singh},\n  title     = {A First-Order Formalization of Commitments and Goals for\n               Planning},\n  booktitle = {AAAI},\n  pages = {697--703},\n  year      = {2013},\n  ee        = {http://www.aaai.org/ocs/index.php/AAAI/AAAI13/paper/view/6371},\n  crossref  = {DBLP:conf/aaai/2013},\n  bibsource = {DBLP, http://dblp.uni-trier.de}\n}\n\n
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\n  \n\n  \n  \n  \n  \n  \n  \n    Hierarchical Planning about Goals and Commitments.\n  \n  \n  \n  \n\n\n  \n  Telang, P.; Meneguzzi, F.;  and Singh, M.\n\n\n  \n\n\n\n  In Proceedings of the Twelfth International Conference on Autonomous Agents and Multiagent Systems, pages 877–884,  2013. \n  \n\n\n\n
\n\n\n\n  \n  \n    \"HierarchicalPaper\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{Telang2013,\n  author = {Pankaj Telang and Felipe Meneguzzi and Munindar Singh},\n  title = {Hierarchical Planning about Goals and Commitments},\n  booktitle = {Proceedings of the Twelfth International Conference on Autonomous\n\tAgents and Multiagent Systems},\n  year = {2013},\n  pages = {877--884},\nabstract = {We consider the problem of relating an agent's internal state (its beliefs and goals) and its social state (its commitments to and from other agents) as a way to develop a comprehensive account of decision making by agents in a multiagent system.  We model this problem in terms of hierarchical task networks (HTNs) and show how HTN planning provides a natural representation and reasoning framework for goals and commitments.  Our approach combines a domain-independent theory capturing the lifecycles of goals and commitments, generic patterns of reasoning, and domain models.  Specifically, our approach shows how each agent may take into account its capabilities, costs, and preferences as it plans its interactions (captured as operations on commitments) with other agents to attempt to achieve its goals.},\n  file = {:http\\://www.meneguzzi.eu/felipe/pubs/aamas-commitments-2013.pdf:PDF},\n  url = {http://www.meneguzzi.eu/felipe/pubs/aamas-commitments-2013.pdf},\n  owner = {meneguzzi},\n  timestamp = {2013.06.02}\n}\n\n
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\n  We consider the problem of relating an agent's internal state (its beliefs and goals) and its social state (its commitments to and from other agents) as a way to develop a comprehensive account of decision making by agents in a multiagent system. We model this problem in terms of hierarchical task networks (HTNs) and show how HTN planning provides a natural representation and reasoning framework for goals and commitments. Our approach combines a domain-independent theory capturing the lifecycles of goals and commitments, generic patterns of reasoning, and domain models. Specifically, our approach shows how each agent may take into account its capabilities, costs, and preferences as it plans its interactions (captured as operations on commitments) with other agents to attempt to achieve its goals.\n
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\n  \n\n  \n  \n  \n  \n  \n  \n    Norm Identification through Plan Recognition.\n  \n  \n  \n  \n\n\n  \n  Oren, N.;  and Meneguzzi, F.\n\n\n  \n\n\n\n  In 15th International Workshop on Coordination, Organizations, Institutions, and Norms, pages 161-175,  2013. \n  \n\n\n\n
\n\n\n\n  \n  \n    \"NormPaper\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{Oren2013:norm-detect,\n  author = {Nir Oren and Felipe Meneguzzi},\n  title = {Norm Identification through Plan Recognition},\n  booktitle = {15th International Workshop on Coordination, Organizations, Institutions, and Norms},\n  year = {2013},\n  pages = {161-175},\n  abstract = {Societal rules, as exemplified by norms, aim to provide a degree of behavioural stability to multi-agent societies. Norms regulate a society using the deontic concepts of permissions, obligations and prohibitions to specify what can, must and must not occur in a society. Many implementations of normative systems assume various combinations of the following assumptions: that the set of norms is static and defined at design time; that agents joining a society are instantly informed of the complete set of norms; that the set of agents within a society does not change; and that all agents are aware of the existing norms. When any one of these assumptions is dropped, agents need a mechanism to identify the set of norms currently present within a society, or risk unwittingly violating the norms. In this paper, we develop a norm identification mechanism that uses a combination of parsing-based plan recognition and Hierarchical Task Network (HTN) planning mechanisms, which operates by analysing the actions performed by other agents. While our basic mechanism cannot learn in situations where norm violations take place, we describe an extension which is able to operate in the presence of violations.},\n  file = {:http\\://www.meneguzzi.eu/felipe/pubs/coin-norm-detect-2013.pdf:PDF},\n  owner = {meneguzzi},\n  timestamp = {2013.05.06},\n  url = {http://www.meneguzzi.eu/felipe/pubs/coin-norm-detect-2013.pdf}\n}\n\n\n
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\n  Societal rules, as exemplified by norms, aim to provide a degree of behavioural stability to multi-agent societies. Norms regulate a society using the deontic concepts of permissions, obligations and prohibitions to specify what can, must and must not occur in a society. Many implementations of normative systems assume various combinations of the following assumptions: that the set of norms is static and defined at design time; that agents joining a society are instantly informed of the complete set of norms; that the set of agents within a society does not change; and that all agents are aware of the existing norms. When any one of these assumptions is dropped, agents need a mechanism to identify the set of norms currently present within a society, or risk unwittingly violating the norms. In this paper, we develop a norm identification mechanism that uses a combination of parsing-based plan recognition and Hierarchical Task Network (HTN) planning mechanisms, which operates by analysing the actions performed by other agents. While our basic mechanism cannot learn in situations where norm violations take place, we describe an extension which is able to operate in the presence of violations.\n
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\n       \n      2012\n      \n        \n        (1)\n        \n      \n    
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\n  \n\n  \n  \n  \n  \n  \n  \n    Applying electronic contracting to the aerospace aftercare domain.\n  \n  \n  \n  \n\n\n  \n  Meneguzzi, F.; Modgil, S.; Oren, N.; Miles, S.; Luck, M.;  and Faci, N.\n\n\n  \n\n\n\n  Engineering Applications of Artificial Intelligence, 25(7): 1471-1487.  2012.\n  \n\n\n\n
\n\n\n\n  \n  \n    \"ApplyingPaper\n   \n  \n\n  \n  \n    doi\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\n\n
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@article{Meneguzzi2012,\ntitle = {Applying electronic contracting to the aerospace aftercare domain},\njournal = {Engineering Applications of Artificial Intelligence},\nvolume    = {25},\nnumber    = {7},\nyear      = {2012},\npages     = {1471-1487},\nissn = {0952-1976},\ndoi = {10.1016/j.engappai.2012.06.004},\nurl = {http://www.sciencedirect.com/science/article/pii/S0952197612001479},\nauthor = {Felipe Meneguzzi and Sanjay Modgil and Nir Oren and Simon Miles and Michael Luck and Noura Faci},\nkeywords = {CONTRACT},\nkeywords = {Norms},\nkeywords = {Monitoring},\nkeywords = {BDI},\nkeywords = {ATN},\nabstract = {The contract project was a European Commission project whose aim was to develop frameworks, components and tools to model, build, verify and monitor distributed electronic business systems based on electronic contracts. In this context, an electronic contract provides a specification of the expected behaviours of individual services, with the assumption that these services are often enacted by autonomous agents. Using the theoretical tools created by the project, in this paper we describe the complete life cycle of instantiating an electronic contracting system using the contract framework within the aerospace aftercare domain. Thus, we use a natural language description of parts of the types of contracts used in this domain to generate individual norms amenable to a computational representation, and how these norms are used to generate a concrete contract monitor. Moreover, we describe a concrete implementation of contract agents in the AgentSpeak(L) language and how these agents interact within a concrete instantiation of contract.}\n}\n\n
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\n  The contract project was a European Commission project whose aim was to develop frameworks, components and tools to model, build, verify and monitor distributed electronic business systems based on electronic contracts. In this context, an electronic contract provides a specification of the expected behaviours of individual services, with the assumption that these services are often enacted by autonomous agents. Using the theoretical tools created by the project, in this paper we describe the complete life cycle of instantiating an electronic contracting system using the contract framework within the aerospace aftercare domain. Thus, we use a natural language description of parts of the types of contracts used in this domain to generate individual norms amenable to a computational representation, and how these norms are used to generate a concrete contract monitor. Moreover, we describe a concrete implementation of contract agents in the AgentSpeak(L) language and how these agents interact within a concrete instantiation of contract.\n
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\n  \n\n  \n  \n  \n  \n  \n  \n    Reports of the AAAI 2010 Fall Symposia.\n  \n  \n  \n  \n\n\n  \n  Azevedo, R.; Biswas, G.; Bohus, D.; Carmichael, T.; Finlayson, M. A.; Hadzikadic, M.; Havasi, C.; Horvitz, E.; Kanda, T.; Koyejo, O.; Lawless, W. F.; Lenat, D. B.; Meneguzzi, F.; Mutlu, B.; Oh, J.; Pirrone, R.; Raux, A.; Sofge, D. A.; Sukthankar, G.;  and Durme, B. V.\n\n\n  \n\n\n\n  AI Magazine, 32(1): 93–100.  2011.\n  \n\n\n\n
\n\n\n\n  \n  \n    \"ReportsPaper\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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@article{DBLP:journals/aim/AzevedoBBCFHHHKKLLMMOPRSSD11,\n  author    = {Roger Azevedo and\n               Gautam Biswas and\n               Dan Bohus and\n               Ted Carmichael and\n               Mark A. Finlayson and\n               Mirsad Hadzikadic and\n               Catherine Havasi and\n               Eric Horvitz and\n               Takayuki Kanda and\n               Oluwasanmi Koyejo and\n               William F. Lawless and\n               Douglas B. Lenat and\n               Felipe Meneguzzi and\n               Bilge Mutlu and\n               Jean Oh and\n               Roberto Pirrone and\n               Antoine Raux and\n               Donald A. Sofge and\n               Gita Sukthankar and\n               Benjamin Van Durme},\n  title     = {Reports of the {AAAI} 2010 Fall Symposia},\n  journal   = {{AI} Magazine},\n  volume    = {32},\n  number    = {1},\n  pages     = {93--100},\n  year      = {2011},\n  url       = {http://www.aaai.org/ojs/index.php/aimagazine/article/view/2338},\n  timestamp = {Thu, 07 Jul 2011 21:51:18 +0200},\n  biburl    = {http://dblp.uni-trier.de/rec/bib/journals/aim/AzevedoBBCFHHHKKLLMMOPRSSD11},\n  bibsource = {dblp computer science bibliography, http://dblp.org}\n}\n\n
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