Nonmonotonic reasoning, preferential models and cumulative logics
Artificial Intelligence
Propositional knowledge base revision and minimal change
Artificial Intelligence
What does a conditional knowledge base entail?
Artificial Intelligence
Nonmonotonic inference based on expectations
Artificial Intelligence
Handbook of logic in artificial intelligence and logic programming (Vol. 4)
On the logic of iterated belief revision
Artificial Intelligence
Possibilistic Merging and Distance-Based Fusion of Propositional Information
Annals of Mathematics and Artificial Intelligence
Resolving Conflicting Information
Journal of Logic, Language and Information
Belief Fusion: Aggregating Pedigreed Belief States
Journal of Logic, Language and Information
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
When plans distinguish Bayes nets
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Automatic ranking of information retrieval systems using data fusion
Information Processing and Management: an International Journal
A split-combination approach to merging knowledge bases in possibilistic logic
Annals of Mathematics and Artificial Intelligence
A Short Introduction to Computational Social Choice
SOFSEM '07 Proceedings of the 33rd conference on Current Trends in Theory and Practice of Computer Science
A general family of preferential belief removal operators
LORI'09 Proceedings of the 2nd international conference on Logic, rationality and interaction
Some representation and computational issues in social choice
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Complexity of judgment aggregation
Journal of Artificial Intelligence Research
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We consider the two-fold problem of representing collective beliefs and aggregating these beliefs. We propose a novel representation for collective beliefs that uses modular, transitive relations over possible worlds. They allow us to represent conflicting opinions and they have a clear semantics, thus improving upon the quasi-transitive relations often used in social choice. We then describe a way to construct the belief state of an agent informed by a set of sources of varying degrees of reliability. This construction circumvents Arrow's Impossibility Theorem in a satisfactory manner by accounting for the explicitly encoded conflicts. We give a simple set-theory-based operator for combining the information of multiple agents. We show that this operator satisfies the desirable invariants of idempotence, commutativity, and associativity, and, thus, is well-behaved when iterated, and we describe a computationally effective way of computing the resulting belief state. Finally, we extend our framework to incorporate voting.