Jeffrey-like rules of conditioning for the dempster-Shafer theory of evidence
International Journal of Approximate Reasoning
Propositional knowledge base revision and minimal change
Artificial Intelligence
Epistemic entrenchment and possibilistic logic
Artificial Intelligence
Nonmonotonic inference based on expectations
Artificial Intelligence
Handbook of logic in artificial intelligence and logic programming (vol. 3)
On the logic of iterated belief revision
Artificial Intelligence
Possibilistic Merging and Distance-Based Fusion of Propositional Information
Annals of Mathematics and Artificial Intelligence
Belief Fusion: Aggregating Pedigreed Belief States
Journal of Logic, Language and Information
Social choice theory, belief merging, and strategy-proofness
Information Fusion
Analyzing the combination of conflicting belief functions
Information Fusion
Three Scenarios for the Revision of Epistemic States*
Journal of Logic and Computation
Can the Minimum Rule of Possibility Theory Be Extended to Belief Functions?
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Defining relative likelihood in partially-ordered preferential structures
Journal of Artificial Intelligence Research
On the revision of probabilistic beliefs using uncertain evidence
Artificial Intelligence
A Framework for Iterated Belief Revision Using Possibilistic Counterparts to Jeffrey's Rule
Fundamenta Informaticae - Methodologies for Intelligent Systems
Revision Rules in the Theory of Evidence
ICTAI '10 Proceedings of the 2010 22nd IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Belief revision within fragments of propositional logic
Journal of Computer and System Sciences
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Fusion and revision are two key topics in knowledge representation and uncertainty theories. However, various formal axiomatisations of these notions were proposed inside specific settings, like logic, probability theory, possibility theory, kappa functions, belief functions and imprecise probability. For instance, the revision rule in probability theory is Jeffrey's rule, and is characterized by two axioms. The AGM axioms for revision are stated in the propositional logic setting. But there is no bridge between these axiomatizations. Likewise, Dempster rule of combination was axiomatized by Smets among others, and a logical syntax-independent axiomatization for merging was independently proposed by Koniezny and Pino-Perez, while a belief function can be viewed as a weighted belief set. Moreover the distinction between fusion and revision is not always so clear and comparing sets of postulates for each of them can be enlightening. This paper presents a tentative set of basic principles for revision and another set of principles for fusion that could be valid regardless of whether information is represented qualitatively or quantitatively. In short, while revision obeys a success postulate and a minimal change principle, fusion is essentially symmetric, and obeys a principle of optimism, that tries to take advantage of all sources of information. Moreover, when two pieces of information are consistent, revising one by the other comes down to merging them symmetrically. Finally, there is a principle of minimal commitment at work in all settings, and common to the two operations.