A guide to completeness and complexity for modal logics of knowledge and belief
Artificial Intelligence
On the semantics of theory change: arbitration between old and new information
PODS '93 Proceedings of the twelfth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Arbitration (or How to Merge Knowledge Bases)
IEEE Transactions on Knowledge and Data Engineering
Model-based belief merging without distance measures
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Implementing PS-Merge Operator
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
Belief merging using normal forms
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
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When information comes from different sources inconsistent beliefs may appear. To handle inconsistency, several model-based belief merging operators have been proposed. Starting from different belief bases which might conflict, these operators return a unique consistent base which represents the beliefs of the group. The operators, parameterized by a distance between interpretations and aggregation function, usually only take into account consistent bases, consequently some information which is not responsible for conflicts may be ignored. An alternative way of merging uses the notion of Partial Satisfiability to define PS-Merge, a model-based merging operator that produces similar results to other merging approaches, but while other approaches require many merging operators in order to achieve satisfactory results for different scenarios PS-Merge obtains similar results for all these different scenarios with a unique operator. This paper analyzes some of the properties satisfied by PS-Merge.