Handbook of logic in artificial intelligence and logic programming (vol. 3)
Integration of weighted knowledge bases
Artificial Intelligence
An egalitarist fusion of incommensurable ranked belief bases under constraints
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Preferred subtheories: an extended logical framework for default reasoning
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Iterated theory base change: a computational model
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
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Different methods have been proposed for merging multiple and potentially conflicting informations. Sum-based operators offer a natural method for merging commensurable prioritized belief bases. Their popularity is due to the fact that they satisfy the majority property and they adopt a non cautious attitude in deriving plausible conclusions. This paper analyses the sum-based merging operator when sources to merge are incommensurable, namely they do not share the same meaning of uncertainty scales. We first show that the obtained merging operator can be equivalently characterized either in terms of an infinite set of compatible scales, or by a well-known Pareto ordering on a set of models. We then study different families of compatible scales useful for merging process. This paper also provides a postulates-based analysis of our merging operators.