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Modelling Multi-Agent Epistemic Planning in ASP

Published online by Cambridge University Press:  21 September 2020

ALESSANDRO BURIGANAOpen the ORCID record for ALESSANDRO BURIGANA[Opens in a new window],
FRANCESCO FABIANO,
AGOSTINO DOVIEROpen the ORCID record for AGOSTINO DOVIER[Opens in a new window] and
ENRICO PONTELLIOpen the ORCID record for ENRICO PONTELLI[Opens in a new window]
ALESSANDRO BURIGANA
Affiliation:
University of Udine, Udine, Italy (e-mail: burigana.alessandro@spes.uniud.it) (e-mail: francesco.fabiano@uniud.it, agostino.dovier@uniud.it)
FRANCESCO FABIANO
Affiliation:
University of Udine, Udine, Italy (e-mail: burigana.alessandro@spes.uniud.it) (e-mail: francesco.fabiano@uniud.it, agostino.dovier@uniud.it)
AGOSTINO DOVIER
Affiliation:
University of Udine, Udine, Italy (e-mail: burigana.alessandro@spes.uniud.it) (e-mail: francesco.fabiano@uniud.it, agostino.dovier@uniud.it)
ENRICO PONTELLI
Affiliation:
New Mexico State University, Las Cruces, NM, USA (e-mail: epontell@cs.nmsu.edu)
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Abstract

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Designing agents that reason and act upon the world has always been one of the main objectives of the Artificial Intelligence community. While for planning in “simple” domains the agents can solely rely on facts about the world, in several contexts, e.g., economy, security, justice and politics, the mere knowledge of the world could be insufficient to reach a desired goal. In these scenarios, epistemic reasoning, i.e., reasoning about agents’ beliefs about themselves and about other agents’ beliefs, is essential to design winning strategies. This paper addresses the problem of reasoning in multi-agent epistemic settings exploiting declarative programming techniques. In particular, the paper presents an actual implementation of a multi-shot Answer Set Programming-based planner that can reason in multi-agent epistemic settings, called PLATO (ePistemic muLti-agent Answer seT programming sOlver). The ASP paradigm enables a concise and elegant design of the planner, w.r.t. other imperative implementations, facilitating the development of formal verification of correctness. The paper shows how the planner, exploiting an ad-hoc epistemic state representation and the efficiency of ASP solvers, has competitive performance results on benchmarks collected from the literature.

Keywords

Epistemic ReasoningMulti-Shot ASPPlanningMulti-AgentPossibilities

Information

Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 20 , Issue 5: 36th International Conference on Logic Programming Special Issue I , September 2020, pp. 593 - 608
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Copyright
© The Author(s), 2020. Published by Cambridge University Press

References

Aczel, P. 1988. Non-well-founded sets. CSLI Lecture Notes, 14, Stanford University, Center for the Study of Language and Information.Google Scholar
Baral, C., Gelfond, G., Pontelli, E., and Son, T. C. 2015. An action language for multi-agent domains: Foundations. CoRR abs/1511.01960.Google Scholar
Baral, C., Gelfond, G., Son, T. and Pontelli, E. 2010. Using answer set programming to model multi-agent scenarios involving agents’ knowledge about other’s knowledge. In Proc of AAMAS, Vol.1, 259-266.Google Scholar
Bolander, T. and Andersen, M. 2011. Epistemic planning for single-and multi-agent systems. Journal of Applied Non-Classical Logics 21, 1, 9–34.CrossRefGoogle Scholar
Castellini, C., Giunchiglia, E., and Tacchella, A. 2001. Improvements to sat-based conformant planning. In Proceedings of 6th European Conference on Planning (ECP-01).Google Scholar
de Weerdt, M., Bos, A., Tonino, H., and Witteveen, C. 2003. A resource logic for multi-agent plan merging. Ann. Math. Artif. Intell. 37, 1-2, 93–130.Google Scholar
de Weerdt, M. and Clement, B. 2009. Introduction to planning in multiagent systems. Multiagent and Grid Systems 5, 4, 345–355.CrossRefGoogle Scholar
Dovier, A. 2015. Logic programming and bisimulation. In ICLP, Vos, M. D., Eiter, T., Lierler, Y., and Toni, F., Eds. CEUR vol. 1433.Google Scholar
Dovier, A., Formisano, A., and Pontelli, E. 2013. Autonomous agents coordination: Action languages meet CLP(FD) and LINDA. Theory Pract. Log. Program. 13, 2, 149–173.CrossRefGoogle Scholar
Durfee, E. H. 1999. Distributed continual planning for unmanned ground vehicle teams. AI Magazine 20, 4, 55–61.Google Scholar
Fabiano, F., Burigana, A., Dovier, A., and Pontelli, E. 2020. EFP 2.0: A multi-Agent Epistemic Solver with Multiple e-State Representations. Proc of ICAPS, 101–109.Google Scholar
Fabiano, F., Riouak, I., Dovier, A., and Pontelli, E. 2019. Non-well-founded set based multi-agent epistemic action language. In Proc of CILC, CEUR vol. 2396, 242–259.Google Scholar
Fagin, R., Halpern, J., Moses, Y., and Vardi, M. 2004. Reasoning about knowledge. MIT press.Google Scholar
Gebser, M., Kaminski, R., Kaufmann, B., and Schaub, T. 2019. Multi-shot asp solving with clingo. Theory and Practice of Logic Programming 19, 27–82.Google Scholar
Gelfond, M. and Lifschitz, V. 1988. The stable model semantics for logic programming. In Proc of ICLP/ILPS, Kowalski, R. A. and Bowen, K. A., Eds. MIT Press, 1070–1080.Google Scholar
Gelfond, M. and Lifschitz, V. 1993. Representing action and change by logic programs. J. Log. Program. 17, 2/3&4, 301–321.Google Scholar
Gelfond, M. and Lifschitz, V. 1998. Action languages. Electron. Trans. Artif. Intell. 2, 193–210.Google Scholar
Gerbrandy, J. and Groeneveld, W. 1997. Reasoning about information change. Journal of Logic, Language and Information 6, 2, 147–169.CrossRefGoogle Scholar
Goldman, C. V. and Zilberstein, S. 2004. Decentralized control of cooperative systems: Categorization and complexity analysis. JAIR 22, 143–174.CrossRefGoogle Scholar
Huang, X., Fang, B., Wan, H., and Liu, Y. 2017. A general multi-agent epistemic planner based on higher-order belief change. In Proc of IJCAI, 1093–1101.Google Scholar
Kominis, F. and Geffner, H. 2015. Beliefs in multiagent planning: From one agent to many. In Proc of ICAPS. 147–155.Google Scholar
Kripke, S. A. 1963. Semantical considerations on modal logic. Acta Philosophica Fennica 16, 1963, 83–94.Google Scholar
Le, T., Fabiano, F., Son, T. C., and Pontelli, E. 2018. EFP and PG-EFP: Epistemic forward search planners in multi-agent domains. In Proc of ICAPS, 161–170.Google Scholar
Löwe, B., Pacuit, E., and Witzel, A. 2011. Del planning and some tractable cases. In International Workshop on Logic, Rationality and Interaction. Springer, 179–192.Google Scholar
Son, T. C., Pontelli, E., Baral, C., and Gelfond, G. 2014. Finitary S5-theories. In European Workshop on Logics in Artificial Intelligence. Springer, 239–252.Google Scholar
Son, T. C., Tu, P. H., Gelfond, M., and Morales, A. R. 2005. An approximation of action theories of and its application to conformant planning. In Proc of LPNMR, Springer, 172–184.Google Scholar
Van Der Hoek, W. and Wooldridge, M. 2002. Tractable multiagent planning for epistemic goals. In Proc of AAMAS: part 3. ACM, 1167–1174.Google Scholar
Van Ditmarsch, H., van Der Hoek, W. and Kooi, B. 2007. Dynamic epistemic logic. Volume 337, Springer Science & Business Media.Google Scholar