Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Possibilistic Merging and Distance-Based Fusion of Propositional Information
Annals of Mathematics and 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
Aggregating partially ordered preferences: impossibility and possibility results
TARK '05 Proceedings of the 10th conference on Theoretical aspects of rationality and knowledge
Judgment aggregation and the problem of truth-tracking
TARK '07 Proceedings of the 11th conference on Theoretical aspects of rationality and knowledge
An egalitarist fusion of incommensurable ranked belief bases under constraints
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Quota and Gmin merging operators
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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Belief merging is often described as the process of defining a base which best represents the beliefs of a group of agents (a profile of belief bases). The resulting base can be viewed as a synthesis of the input profile. In this paper another view of what belief merging aims at is considered: the epistemic view. Under this view the purpose of belief merging is to best approximate the true state of the world. We point out a generalization of Condorcet's Jury Theorem from the belief merging perspective. Roughly, we show that if the beliefs of sufficiently many reliable agents are merged then in the limit the true state of the world is identified. We introduce a new postulate suited to the truth tracking issue. We identify some merging operators from the literature which satisfy it and other operators which do not.