The size of a revised knowledge base
Artificial Intelligence
Combining Multiple Knowledge Bases
IEEE Transactions on Knowledge and Data Engineering
Arbitration (or How to Merge Knowledge Bases)
IEEE Transactions on Knowledge and Data Engineering
Artificial Intelligence - Special issue on nonmonotonic reasoning
The strategy-proofness landscape of merging
Journal of Artificial Intelligence Research
Preferred subtheories: an extended logical framework for default reasoning
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Proving theorems in a multi-source environment
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Merging interval-based possibilistic belief bases
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
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When aggregating information from a group of agents, accepting the pieces of information shared by all agents is a natural requirement. In this paper, we investigate such a unanimity condition in the setting of propositional merging. We discuss two interpretations of the unanimity condition. We show that the first interpretation is captured by existing postulates for merging. But the second interpretation is not, and this leads to the introduction of a new disjunction postulate (Disj). It turns out that existing operators satisfying (Disj) do not perform well with respect to the standard criteria used to evaluate merging operators: logical properties, computational complexity and strategy-proofness. To fill this gap, we introduce two new families of propositional merging operators, quota operators and Gmin operators, which satisfy (Disj), and achieve interesting trade-offs with respect to the logical, computational, and strategy-proofness criteria.