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Modeling knowledge dynamics in multi-agent systems based on informants

Published online by Cambridge University Press:  22 February 2012

Luciano H. Tamargo ,
Alejandro J. García ,
Marcelo A. Falappa  and
Guillermo R. Simari
Luciano H. Tamargo*
Affiliation:
Department of Computer Science and Engineering, Artificial Intelligence Research and Development Laboratory, Universidad Nacional del Sur, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Bahía Blanca, Argentina
Alejandro J. García*
Affiliation:
Department of Computer Science and Engineering, Artificial Intelligence Research and Development Laboratory, Universidad Nacional del Sur, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Bahía Blanca, Argentina
Marcelo A. Falappa*
Affiliation:
Department of Computer Science and Engineering, Artificial Intelligence Research and Development Laboratory, Universidad Nacional del Sur, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Bahía Blanca, Argentina
Guillermo R. Simari*
Affiliation:
Department of Computer Science and Engineering, Artificial Intelligence Research and Development Laboratory, Universidad Nacional del Sur, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Bahía Blanca, Argentina
*
e-mails: [email protected], [email protected], [email protected], [email protected]
e-mails: [email protected], [email protected], [email protected], [email protected]
e-mails: [email protected], [email protected], [email protected], [email protected]
e-mails: [email protected], [email protected], [email protected], [email protected]
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Abstract

In this paper, we model knowledge dynamics in agents’ belief bases in a collaborative multi-agent system (MAS). Four change operators are introduced: expansion, contraction, prioritized revision, and non-prioritized revision. For all of them, both constructive definitions and an axiomatic characterization by representation theorems are given. We formally justify minimal change, consistency maintenance, and non-prioritization principles. These operators are based on an epistemic model for multi-source belief revision in which a rational way to weigh the beliefs using a credibility order among agents is developed. The defined operators can be seen as skills added to the agents improving the collective reasoning of a MAS.

Type
Articles
Information
The Knowledge Engineering Review , Volume 27 , Issue 1 , 22 February 2012 , pp. 87 - 114
Copyright
Copyright © Cambridge University Press 2012

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References

Alchourrón, C., Makinson, D. 1985. On the logic of theory change: safe contraction. Studia Logica 44, 405–422.Google Scholar
Alchourrón, C., Gärdenfors, P., Makinson, D. 1985. On the logic of theory change: partial meet contraction and revision functions. Journal of Symbolic Logic 50(2), 510–530.CrossRefGoogle Scholar
Benferhat, S., Dubois, D., Prade, H. 1993. Argumentative inference in uncertain and inconsistent knowledge bases. In The Nineteenth Conference on Uncertainty in Artificial Intelligence (UAI'03), 411–419. Morgan Kaufmann.Google Scholar
Benferhat, S., Dubois, D., Prade, H., Williams, M. A. 2002. A practical approach to revising prioritized knowledge bases. Studia Logica 70(1), 105–130.Google Scholar
Cantwell, J. 1998. Resolving conflicting information. Journal of Logic, Language and Information 7(2), 191–220.Google Scholar
Dalal, M., 1988. Investigations into a theory of knowledge base revision. Proceedings of Seventh International Conference on Artificial Intelligence, (AAAI-88), 475–479.Google Scholar
Dragoni, A., Giorgini, P., Puliti, P. 1994. Distributed belief revision versus distributed truth maintenance. In Proceedings of the Sixth IEEE International Conference on Tools with Artificial Intelligence (TAI 94), 499–505. IEEE Computer Society Press.Google Scholar
Dragoni, A., Giorgini, P., Baffetti, M. 1997. Distributed belief revision vs. belief revision in a multi-agent environment: first results of a simulation experiment. In Multi-agent Rationality, Boman M. & Van de Velde W. (eds.) Springer-Verlag, 45–62.Google Scholar
Falappa, M. A., Kern-Isberner, G., Simari, G. R. 2002. Explanations, belief revision and defeasible reasoning. Artificial Intelligence 141(1), 1–28.Google Scholar
Fermé, E. L., Hansson, S. O. 1999. Selective revision. Studia Logica 63(3), 331–342.Google Scholar
Fuhrmann, A. 1991. Theory contraction through base contraction. Journal of Philosophical Logic 20(2), 175–203.Google Scholar
Gärdenfors, P. 1981. An epistemic approach to conditionals. American Philosophical Quarterly 18(3), 203–211.Google Scholar
Gärdenfors, P. 1992. Belief Revision, chapter Belief Revision: an introduction Cambridge University Press, 1–28.Google Scholar
Gärdenfors, P., Makinson, D. 1988. Revisions of knowledge systems using epistemic entrenchment. In Second Conference on Theoretical Aspects of Reasoning about Knowledge Conference, 83–95.Google Scholar
Hansson, S. O. 1992. In defense of base contraction. Synthese 91(3), 239–245.CrossRefGoogle Scholar
Hansson, S. O. 1994. Kernel contraction. Journal of Symbolic Logic 59(3), 845–859.Google Scholar
Hansson, S. O. 1997. Semi-revision. Journal of Applied Non-Classical Logic 151–175.Google Scholar
Hansson, S. O. 1999. A Textbook of Belief Dynamics: Theory Change and Database Updating. Kluwer Academic Publishers.Google Scholar
Hansson, S. O., Fermé, E., Cantwell, J., Falappa, M. 2001. Credibility limited revision. The Journal of Symbolic Logic 66(4), 1581–1596.CrossRefGoogle Scholar
Hein, J. L. 2010. Discrete Structures, Logic, and Computability, Chapter Equivalence, Order, and Inductive Proof, third edition. Jones and Bartlett.Google Scholar
Kfir-Dahav, N. E., Tennenholz, M. 1996. Multi-agent belief revision. In Theoretical Aspects of Rationality and Knowledge: Proceeding of the Sixth Conference (TARK 1996). Morgan Kaufmann Publishers Inc. 175–196.Google Scholar
Konieczny, S., Grespan, M. M., Pérez, R. P. 2010. Taxonomy of improvement operators and the problem of minimal change. In Twelfth International Conference on Principles of Knowledge Representation and Reasoning (KR'10).Google Scholar
Krümpelmann, P., Tamargo, L. H., García, A. J., Falappa, M. A. 2009. Forwarding credible information in multi-agent systems. Proceedings of the 3rd International Conference on Knowledge Science, Engineering and Management (KSEM 2009), 5914/2009: 41–53.Google Scholar
Liu, W., Williams, M-A. 2001. A framework for multi-agent belief revision. Studia Logica 67(2), 291–312.Google Scholar
Liu, W., Williams, M-A. 1999. A framework for multi-agent belief revision, part i: the role of ontology. In Australian Joint Conference on Artificial Intelligence, 168–179.Google Scholar
Makinson, D. 1997. Screened revision. Theoria: Special Issue on Non-Prioritized Belief Revision.Google Scholar
Malheiro, B., Jennings, N., Oliveira, E. 1994. Belief revision in multi-agent systems. Proceeding of the 11th European Conference on Artificial Intelligence (ECAI 94), 294–298.Google Scholar
Rott, H. 1992. Preferential belief change using generalized epistemic entrenchment. Journal of Logic, Language and Information 1(1), 45–78.Google Scholar
Tamargo, L. H., García, A. J., Falappa, M. A., Simari, G. R. 2008. Consistency maintenance of plausible belief bases based on agents credibility. 12th International Workshop on Non-Monotonic Reasoning (NMR), 50–58.Google Scholar
Tamargo, L. H., García, A. J., Falappa, M. A., Simari, G. R. 2009. A belief revision approach to inconsistency handling in multi-agent systems. The IJCAI-09 Workshop on Nonmonotonic Reasoning, Action and Change (NRAC), 63–70.Google Scholar