Propositional knowledge base revision and minimal change
Artificial Intelligence
Qualitative probabilities for default reasoning, belief revision, and causal modeling
Artificial Intelligence
On the logic of iterated belief revision
Artificial Intelligence
A Thorough Axiomatization of a Principle of Conditional Preservation in Belief Revision
Annals of Mathematics and Artificial Intelligence
Iterated belief revision, revised
Artificial Intelligence
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Conditionals in nonmonotonic reasoning and belief revision: considering conditionals as agents
Conditionals in nonmonotonic reasoning and belief revision: considering conditionals as agents
Revision over partial pre-orders: a postulational study
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
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Recent years have seen a lot of work towards extending the established AGM belief revision theory with respect to iterating revision, preserving conditional beliefs, and handling sets of propositions as new information. In particular, novel postulates like independence and evidence retainment have been brought forth as new standards for revising epistemic states by (sets of) propositional information. In this paper, we propose a constructive approach for revising epistemic states by sets of (propositional and conditional) beliefs that combines ideas from nonmonotonic reasoning with conditional belief revision. We also propose a novel principle called enforcement that covers both independence and evidence retainment, and we show our revision operator to comply with major postulates from the literature. Moreover, we point out the relevance of our approach for default reasoning.