Handbook of logic in artificial intelligence and logic programming (vol. 3)
Integration of weighted knowledge bases
Artificial Intelligence
Possibilistic Merging and Distance-Based Fusion of Propositional Information
Annals of Mathematics and Artificial Intelligence
A Practical Approach to Fusing Prioritized Knowledge Bases
EPIA '99 Proceedings of the 9th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Merging stratified knowledge bases under constraints
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Possibilistic information fusion using maximal coherent subsets
IEEE Transactions on Fuzzy Systems
Disjunctive merging: Quota and Gmin merging operators
Artificial Intelligence
A comparison of merging operators in possibilistic logic
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
Generalized possibilistic logic
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
A principled analysis of merging operations in possibilistic logic
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Interval-based possibilistic logic
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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In the last decade, several approaches were introduced in literature to merge multiple and potentially conflicting pieces of information. Within the growing field of application favourable to distributed information, data fusion strategies aim at providing a global and consistent point of view over a set of sources which can contradict each other. Moreover, in many situations, the pieces of information provided by these sources are uncertain. Possibilistic logic is a well-known powerful framework to handle such kind of uncertainty where formulas are associated with real degrees of certainty belonging to [0,1]. Recently, a more flexible representation of uncertain information was proposed, where the weights associated with formulas are in the form of intervals. This interval-based possibilistic logic extends classical possibilistic logic when all intervals are singletons, and this flexibility in representing uncertain information is handled without extra computational costs. In this paper, we propose to extend a well known approach of possibilistic merging to the notion of interval-based possibilistic knowledge bases. We provide a general semantic approach and study its syntactical counterpart. In particular, we show that convenient and intuitive properties of the interval-based possibilistic framework hold when considering the belief merging issue.