Propositional knowledge base revision and minimal change
Artificial Intelligence
On the logic of iterated belief revision
Artificial Intelligence
Revision in Extended Propositional Calculus
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
System Z: a natural ordering of defaults with tractable applications to nonmonotonic reasoning
TARK '90 Proceedings of the 3rd conference on Theoretical aspects of reasoning about knowledge
Revision sequences and nested conditionals
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Iterated theory base change: a computational model
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A Practical Approach to Fusing Prioritized Knowledge Bases
EPIA '99 Proceedings of the 9th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Confidence Relations as a Basis for Uncertainty Modeling, Plausible Reasoning, and Belief Revision
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Some Operators for Iterated Revision
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Making revision reversible: an approach based on polynomials
Fundamenta Informaticae
Matrix representation of belief states: an algebraic semantics for belief logics
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Ordinal and probabilistic representations of acceptance
Journal of Artificial Intelligence Research
Acceptance, conditionals, and belief revision
WCII'02 Proceedings of the 2002 international conference on Conditionals, Information, and Inference
Making revision reversible: an approach based on polynomials
Fundamenta Informaticae
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This paper deals with iterated belief change and proposes a drastic revision rule that modifies a plausibility ordering of interpretations in such a way that any world where the input observartion holds is more plausible that any world where it does not. This change rule makes sense in a dynamic context where observations are received, and the newer observations are considered more plausible than older ones. It is shown how to encode an epistemic state using polynomials equipped with the lexicographical ordering. This encoding makes it very easy to implement and iterate the revision rule using simple operations on these polynomials. Moreover, polynomials allow to keep track of the sequence of observations. Lastly, it is shown how to efficiently compute the revision rule at the syntactical level, when the epistemic state is concisely represented by a prioritized belief base. Our revision rule is the most drastic one can think of, in accordance with Darwiche and Pearl's principles, and thus contrasts with the minimal change rule called natural belief revision.