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Logic programming with social features1

Published online by Cambridge University Press:  01 November 2008

FRANCESCO BUCCAFURRI  and
GIANLUCA CAMINITI
FRANCESCO BUCCAFURRI
Affiliation:
DIMET—Università “Mediterranea” degli Studi di Reggio Calabria via Graziella, loc. Feo di Vito, 89122 Reggio Calabria, Italia (e-mail: [email protected], [email protected])
GIANLUCA CAMINITI
Affiliation:
DIMET—Università “Mediterranea” degli Studi di Reggio Calabria via Graziella, loc. Feo di Vito, 89122 Reggio Calabria, Italia (e-mail: [email protected], [email protected])
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Abstract

In everyday life it happens that a person has to reason out what other peoplethink and how they behave, in order to achieve his goals. In other words, anindividual may be required to adapt his behavior by reasoning about the others'mental state. In this paper we focus on a knowledge-representation languagederived from logic programming which both supports the representation of mentalstates of individual communities and provides each with the capability ofreasoning about others' mental states and acting accordingly. The proposedsemantics is shown to be translatable into stable model semantics of logicprograms with aggregates.

Keywords

Logic programmingstable model semanticsknowledge representation
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 8 , Issue 5-6 , November 2008 , pp. 643 - 690
Copyright
Copyright © Cambridge University Press 2008

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References

Alberti, M., Chesani, F., Gavanelli, M., Lamma, E., Mello, P. and Torroni, P. 2004. The SOCS computational logic approach to the specification and verification of agent societies. In Global Computing. LNCS. Springer, Berlin/Heidelberg, 314–339.Google Scholar
Alferes, J. J., Leite, J. A., Pereira, L. M., Przymusinska, H. and Przymusinski, T. C. 2000. Dynamic updates of non-monotonic knowledge bases. Journal of Logical Programming 45 (1–3), 43–70.CrossRefGoogle Scholar
Alferes, J. J., Leite, J. A., Pereira, L. M., Przymusinska, H. and Przymusinski, T. C. 2002. A language for multi-dimensional updates. Electronic Notes in Theoretical Computer Science 70 (5), 20–38.Google Scholar
Baral, C. 2003. Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press, Cambridge, UK.CrossRefGoogle Scholar
Bracciali, A., Mancarella, P., Stathis, K. and Toni, F. 2004. On modelling multi-agent systems declaratively. In Declarative Agent Languages and Technologies DALT, Leite, J., Omicini, A., Torroni, P. and Yolum, P., Eds. Lecture Notes in Computer Science, vol. 3476. Springer, Berlin/Heidelberg, 53–68.Google Scholar
Brézillon, P. 1999. Context in problem solving: A survey. The Knowledge Engineering Review 14 (1), 1–34.Google Scholar
Buccafurri, F. and Caminiti, G. 2005. A social semantics for multi-agent systems. In Proceedings of 8th International Conference, LPNMR 2005, Diamante, Italy, Baral, C., Greco, G., Leone, N., and Terracina, G., Eds. LNAI, vol. 3662. Springer-Verlag, Berlin Heidelberg, 317–329.Google Scholar
Buccafurri, F. and Gottlob, G. 2002. Multiagent Compromises, Joint Fixpoints, and Stable Models. LNCS and LNAI, vol. 2407. Springer, Berlin/Heidelberg.Google Scholar
Buvač, S. and Mason, I. 1993. Propositional logic of context. In Proceedings of the Eleventh National Conference on Artificial Intelligence, Fikes, R. and Lehnert, W., Eds. American Association for Artificial Intelligence, AAAI Press, Menlo Park, California, 412–419.Google Scholar
Cohen, P. R. and Levesque, H. 1990. Rational interaction as the basis for communication. In Intentions in Communication. MIT Press, Cambridge, MA.Google Scholar
Cost, R. S., Finin, T. and Labrou, Y. 2001. Coordinating Agents Using ACL Conversations. In Coordination of Internet Agents: Models, Technologies, and Applications. 183–196.Google Scholar
Costantini, S. and Tocchio, A. 2002. A logic programming language for multi-agent systems. In Proceedings of the European Conference on Logics in Artificial Intelligence, (JELIA 2002). LNCS. Springer, Berlin/Heidelberg, 1–13.Google Scholar
Dell'Armi, T., Faber, W., Ielpa, G., Leone, N. and Pfeifer, G. 2003. Aggregate functions in disjunctive logic programming: Semantics, complexity, and implementation in DLV. In IJCAI-03, Proceedings of the 18th International Joint Conference on Artificial Intelligence, Acapulco, Mexico, 847–852.Google Scholar
De Saeger, S. and Shimojima, A. 2006. Contextual reasoning in agent systems. In Proceedings of Computational Logic in Multi-Agent Systems (CLIMA-VII), Hakodate, Japan.Google Scholar
De Vos, M. 2003. An ordered choice logic programming front-end for answer set solvers. In Proceedings of International Joint Conference on Declarative Programming (APPIA-GULP-PRODE). LNCS. Springer, Berlin/Heidelberg, 362–373.Google Scholar
De Vos, M., Crick, T., Padget, J., Brain, M., Cliffe, O. and Needham, J. 2005. LAIMA: A multi-agent platform using ordered choice logic programming. In Proceedings of the International Workshop Declarative Agent Languages and Technologies (DALT 2005). LNCS. Springer, Berlin/Heidelberg, 72–88.Google Scholar
Eiter, T., Gottlob, G. and Mannila, H. 1997. Disjunctive Datalog. ACM Transactions on Database Systems 22 (3), 364–418.Google Scholar
Gelfond, M. and Lifschitz, V. 1988. The stable model semantics for logic programming. In 5th Conference on Logic Programming. MIT Press, Cambridge, MA, 1070–1080.Google Scholar
Gelfond, M. and Lifschitz, V. 1991. Classical negation in logic programs and disjunctive databases. New Generation Computation 9 (3/4), 365–386.Google Scholar
Ghidini, C. and Giunchiglia, F. 2001. Local models semantics, or contextual reasoning = locality + compatibility. Artificial Intelligence 127 (2), 221–259.Google Scholar
Konolige, K. 1984. A Deduction Model of Belief and its Logics. Ph.D. Thesis, Stanford University CA.Google Scholar
Leite, J. A., Alferes, J. J. and Pereira, L. M. 2002. MINERVA: A dynamic logic programming agent architecture. In Proceedings of ATAL-2001. LNAI, Springer, Berlin/Heidelberg, 141–157.Google Scholar
Leone, N., Pfeifer, G., Faber, W., Eiter, T., Gottlob, G., Perri, S. and Scarcello, F. 2002. The DLV System for Knowledge Representation and Reasoning. ArXiv Computer Science e-prints, 11004–+.Google Scholar
Mascardi, V., Martelli, M. and Sterling, L. 2004. Logic-based specification languages for intelligent software agents. Theory and Practice of Logic Programming 4 (4), 429–494.Google Scholar
Mayfield, J., Yannis, L. and Finin, T. 1995. Evaluation of kqml as an agent communication language. In Proceedings of the 2nd International Workshop on Agent Theories, Architectures, and Languages (ATAL'95), Wooldridge, M. J. P., M., and Tambe, M., Eds. Number 1037 in LNAI. Springer-Verlag, Berlin/Heidelberg, 347–360.Google Scholar
McCarthy, J. 1993. Notes on formalizing contexts. In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, Bajcsy, R., Ed. Morgan Kaufmann, San Mateo, California, 555–560.Google Scholar
Rao, A. S. 1996. AgentSpeak(L): BDI Agents Speak Out in a Logical Computable Language. In Agents Breaking Away, Van de Velde, W. and Perram, J. W., Eds. vol. 1038. LNAI. Springer–Verlag, Berlin/Heidelberg, 42–55.Google Scholar
Rao, A. S. and Georgeff, M. 1995. Bdi agents: From theory to practice. In Proceedings of the 1st International Conference on Multi Agent Systems (ICMAS'95), Lesser, V., Ed. AAAI Press, Cambridge, MA, 312–319.Google Scholar
Satoh, K. and Yamamoto, K. 2002. Speculative computation with multi-agent belief revision. In The First International Joint Conference on Autonomous Agents & Multiagent Systems. ACM Press, New York, 897–904.Google Scholar
Serafini, L. and Bouquet, P. 2004. Comparing formal theories of context in ai. Artificial Intelligence 155 (1–2), 41–67.Google Scholar
Subrahmanian, V., Bonatti, P., Dix, J., Eiter, T., Kraus, S., Ozcan, F. and Ross, R. 2000. Heterogeneous Agent Systems. MIT Press/AAAI Press, Cambridge, MA.Google Scholar
van der Hoek, W. and Wooldrige, W. 2003. Towards a logic of rational agency. Logic Journal of the IGPL 11 (2), 135–159.Google Scholar
Wooldridge, M. 2000. Reasoning about Rational Agents. Intelligent Robots and Autonomous Agents. MIT Press, Cambridge, MA.Google Scholar
Wooldridge, M. and Jennings, N. R. 1995. Intelligent agents: Theory and practice. The Knowledge Engineering Review 2 (10), 115–152.Google Scholar