Weighted fuzzy pattern matching
Fuzzy Sets and Systems - Mathematical Modelling
Data fusion in robotics and machine intelligence
Data fusion in robotics and machine intelligence
Handbook of logic in artificial intelligence and logic programming (vol. 3)
Possibilistic Merging and Distance-Based Fusion of Propositional Information
Annals of Mathematics and Artificial Intelligence
Arbitration (or How to Merge Knowledge Bases)
IEEE Transactions on Knowledge and Data Engineering
Quasi-possibilistic logic and its measures of information and conflict
Fundamenta Informaticae
Measuring conflict and agreement between two prioritized belief bases
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
On the Definition of Essential and Contingent Properties of Subjective Belief Bases
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Knowledge and Information Systems
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In this paper, we propose an adaptive algorithm for merging n (⩾2) prioritized knowledge bases which takes into account the degrees of conflict and agreement among these knowledge bases. The algorithm first selects largely partially maximal consistent subsets (LPMCS) of sources by assessing how (partially) consistent the information in the subset is. Then within each of these created subsets, a maximal consistent subset is further selected and knowledge bases in it are merged with a suitable conjunctive operator based on the degree of agreement among them. This result is then merged with the remaining knowledge bases in the corresponding LPMCS in the second step through the relaxation of the minimum operator. Finally, the knowledge bases obtained from the second step are merged by a maximum operator. In comparison with other merging methods, our approach is more context dependent and is especially useful when most sources of information are in conflict.