Abstract
In this paper, a distributed approach to belief revision is presented. It is conceived as a collective activity of a group of interacting agents, in which each component contributes with its own local beliefs. The integration of the different opinions is performed not by an external supervisor, but by the entire group through an election mechanism. Each agent exchanges information with the other components and uses a local belief revision mechanism to maintain its cognitive state consistent. We propose a model for local belief revision/integration based on what we called: “Principle of Recoverability.” Computationally, our way to belief revision consists of three steps acting on the symbolic part of the information, so as to deal with consistency and derivation, and two other steps working with the numerical weight of the information, so as to deal with uncertainty. In order to evaluate and compare the characteristics and performance of the centralized and of the distributed approaches, we made five different experiments simulating a simple society in which each agent is characterized by a degree of competence, communicates with some others, and revise its cognitive state. The results of these experiments are presented in the paper.
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References
L. B. Resnick, “Shared cognition: Thinking as social practice, ” in L. B. Resnick, J. M. Levine, and S. D. Teasly (eds.), Perspectives on Socially Shared Cognition, American Psychological Association: Washington, DC, 1991.
A. F. Dragoni and P. Giorgini, “Sensor data validation for nuclear power plants through bayesian conditioning and Dempster's rule of combination, ” Computers and Artificial Intelligence, vol. 17, no.2–3, pp. 151–168, 1998.
A. F. Dragoni and P. Giorgini, “Belief revision through the belief function formalism in a multi-agent environment, ” in Intelligent Agents III, LNAI, vol. 1193, Springer-Verlag, 1997.
A. F. Dragoni, P. Giorgini, and M. Baffetti, “Distributed belief revision vs. belief revision in a multi-agent environment, ” in M. Boman and W. Van de Welde (eds.), Multi-Agent Rationality, LNAI vol. 1237, Springer-Verlag, 1997.
A. F. Dragoni and S. Animali, “Maximal consistency theory of evidence and Bayesian conditioning in the investigative domain, ” Cybernetics and Systems: an International Journal, vol. 34, no.3–5, June, 2003.
C. E. Alchourrn, P. Gärdenfors, and D. Makinson, “On the logic of theory change: Partial meet contraction and revision functions, ” The Journal of Symbolic Logic, vol. 50, pp. 510–530, 1985.
P. Gärdenfors, Knowledge in Flux: Modeling the Dynamics of Epistemic States, MIT Press: Cambridge, MA., 1988.
P. Gärdenfors, Belief Revision, Cambridge University Press, 1992.
M. A. Williams and H. Roth, Frontiers in Belief Revision, Kluwer Academic Publishers, 2000.
B. Nebel, “Base revision operations and schemes: Semantics, representation, and complexity, ” in Proceedings of the 11th European Conference on Artificial Intelligence (ECAI 94), John Wiley & Sons. 1994.
S. Benferhat, C. Cayrol, D. Dubois, J. Lang, and H. Prade: “Inconsistency management and prioritized syntax-based entailmen, ” in Proceedings of the 13th Inter. Joint Conf. on Artificial Intelligence (IJCAI 93), 1993, pp. 640–645.
D. Dubois and H. Prade, “A survey of belief revision and update rules in various uncertainty models, ”International Journal of Intelligent Systems, vol. 9, pp. 61–100, 1994.
A. F. Dragoni, “Belief revision: From theory to practice, ” The Knowledge Engineering Review, vol. 12, no.2, pp. 147–179, 1997.
A. F. Dragoni. F. Mascaretti, and P. Puliti, “A generalized approach to consistency-based belief revision, ” in M. Gori and G. Soda (eds.), Topics in Artificial Intelligence, LNAI, vol. 992, Springer-Verlag, 1995.
A. F. Dragoni and P. Giorgini, “Distributed knowledge revision-integration, ” in Proceedings of the Sixth ACM International Conference on Information Technology and Management, Las Vegas, ACM Press, 1997.
M. A. Williams, “Iterated theory base change: A computational model, ” in Proceedings of the 14th Inter. Joint Conf. on Artificial Intelligence” (IJCAI 95), 1995, pp. 1541–1547.
A. F. Dragoni, “A model for belief revision in a multi-agent environment, ” in E. Werner and Y. Demazeau (eds.), Decentralized A. I. 3, North Holland, Elsevier Science, 1992.
M. Dalal, “Investigations into a theory of knowledge base revision, ” in Proceedings of the 7th National Conf. on Artificial Intelligence, 1988, pp. 475–479.
J. de Kleer, “An assumption based truth maintenance system, ” Artificial Intelligence, vol. 28, pp. 127–162, 1986.
R. Reiter, “A theory of diagnosis from first principles, ” Artificial Intelligence, vol. 32, no.1, pp. 57–95, 1987.
G. Shafer and R. Srivastava, “The Bayesian and belief-function formalisms: A General Perspective for Auditing, ” in G. Shafer and J. Pearl (eds.), Readings in Uncertain Reasoning, Morgan Kaufmann, 1990.
K.-C. Ng and B. Abramson, “Consensus diagnosis: A simulation study, ” IEEE Transactions on Systems, Man,and Cybernetics, vol. 22, no.5, pp. 916–928, 1992.
I. Bloch, “Information combination operators for data fusion: A comparative review with classication, ” IEEE Transactions on Systems,Man,and Cybernetics, vol. 26, no.1, pp. 52–67, 1996.
D. M. Buede and P. Girardi, “A target identification comparison of bayesian and Dempster-Shafer multisensor fusion, ” IEEE Transactions on Systems,Man,and Cybernetics, vol. 27, no.5, pp. 569–577, 1997.
M. Baffetti, Un'architettura Distribuita per la Revisione/Integrazione di Conoscenze Incerte, Master Thesis (in Italian), Universit di Ancona, 1997.
P. Giorgini, Belief Revision in Multi-Agent Systems. Ph.D. Thesis, Universit di Ancona, 1998.
C. Mason, “An intelligent assistant for nuclear test ban treaty verification, ” IEEE Expert, vol. 10, no.6, 1995.
M. N. Huhns and D. M. Bridgeland, “Distributed truth maintenance, ” in S. M. Dean (ed.), Cooperating Knowledge Based Systems, Springer-Verlag, 1990, pp. 133–147.
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Dragoni, A.F., Giorgini, P. Distributed Belief Revision. Autonomous Agents and Multi-Agent Systems 6, 115–143 (2003). https://doi.org/10.1023/A:1021833301185
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DOI: https://doi.org/10.1023/A:1021833301185