Artificial Intelligence
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Artificial Intelligence
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A model for belief revision in a multi-agent environment (abstract)
ACM SIGOIS Bulletin
Distributed knowledge revision/integration
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Belief Revision
An Intelligent Assistant for Nuclear Test Ban Treaty Verification
IEEE Expert: Intelligent Systems and Their Applications
Proceedings of the 8th European Workshop on Modelling Autonomous Agents in a Multi-Agent World: Multi-Agent Rationality
A Generalized Approach to Consistency Based Belief Revision
AI*IA '95 Proceedings of the 4th Congress of the Italian Association for Artificial Intelligence on Topics in Artificial Intelligence
Belief Revision Through the Belief-Function Formalism in a Multi-Agent Environment
ECAI '96 Proceedings of the Workshop on Intelligent Agents III, Agent Theories, Architectures, and Languages
Belief revision: from theory to practice
The Knowledge Engineering Review
Iterated theory base change: a computational model
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Information combination operators for data fusion: a comparative review with classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A target identification comparison of Bayesian and Dempster-Shafer multisensor fusion
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Using Policies for Information Valuation to Justify Beliefs
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Expressing Belief Flow in Assertion Networks
Logic, Language, and Computation
ArgMAS'04 Proceedings of the First international conference on Argumentation in Multi-Agent Systems
An argumentation-based approach to cooperative multi-source epistemic conflict resolution
MATES'12 Proceedings of the 10th German conference on Multiagent System Technologies
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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.