A knowledge level analysis of belief revision
Proceedings of the first international conference on Principles of knowledge representation and reasoning
Epistemic entrenchment and possibilistic logic
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
Plausibility measures and default reasoning
Journal of the ACM (JACM)
A Note on the Rational Closure of Knowledge Bases with Both Positive and Negative Knowledge
Journal of Logic, Language and Information
Revisions of Knowledge Systems Using Epistemic Entrenchment
Proceedings of the 2nd Conference on Theoretical Aspects of Reasoning about Knowledge
Postulates for Conditional Belief Revision
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Epistemic entrenchment with incomparabilities and relational belief revision
Proceedings of the Workshop on The Logic of Theory Change
Proceedings of the Workshop on The Logic of Theory Change
Dynamic belief revision operators
Artificial Intelligence
Iterated theory base change: a computational model
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Theoretical Computer Science - Logic, language, information and computation
The measurement of ranks and the laws of iterated contraction
Artificial Intelligence
The process of reaching agreement in meaning negotiation
Transactions on Computational Collective Intelligence VII
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Since the early 1980s, logical theories of belief revision have offered formal methods for the transformation of knowledge bases or "corpora" of data and beliefs. Early models have dealt with unconditional acceptance and integration of potentially belief-contravening pieces of information into the existing corpus. More recently, models of "non-prioritized" revision were proposed that allow the agent rationally to refuse to accept the new information. This paper introduces a refined method for changing beliefs by specifying constraints on the relative plausibility of propositions. Like the earlier belief revision models, the method proposed is a qualitative one, in the sense that no numbers are needed in order to specify the posterior plausibility of the new information. We use reference beliefs in order to determine the degree of entrenchment of the newly accepted piece of information. We provide two kinds of semantics for this idea, give a logical characterization of the new model, study its relation with other operations of belief revision and contraction, and discuss its intuitive strengths and weaknesses.