Propositional knowledge base revision and minimal change
Artificial Intelligence
Reasoning with qualitative probabilities can be tractable
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Qualitative probabilities: a normative framework for commonsense reasoning
Qualitative probabilities: a normative framework for commonsense reasoning
A general non-probabilistic theory of inductive reasoning
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Relevance sensitive belief structures
Annals of Mathematics and Artificial Intelligence
Journal of Logic, Language and Information
AI '99 Proceedings of the 12th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
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
Dynamic belief revision operators
Artificial Intelligence
Belief revision: from theory to practice
The Knowledge Engineering Review
Changing conditional beliefs unconditionally
TARK '96 Proceedings of the 6th conference on Theoretical aspects of rationality and knowledge
Distance semantics for belief revision
TARK '96 Proceedings of the 6th conference on Theoretical aspects of rationality and knowledge
SAICSIT '06 Proceedings of the 2006 annual research conference of the South African institute of computer scientists and information technologists on IT research in developing countries
Iterated belief revision, revised
Artificial Intelligence
JELIA '08 Proceedings of the 11th European conference on Logics in Artificial Intelligence
Expressing Belief Flow in Assertion Networks
Logic, Language, and Computation
An inconsistency tolerant model for belief representation and belief revision
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Iterated belief revision, revised
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Iterated theory base change: a computational model
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
The complexity of theory revision
Artificial Intelligence
Logic-based fusion of complex epistemic states
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Belief revision with uncertain inputs in the possibilistic setting
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
On the relation between kappa calculus and probabilistic reasoning
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
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We show in this paper that the AGM postulates are too weak to ensure the rational preservation of conditional beliefs during belief revision, thus permitting improper responses to sequences of observations. We remedy this weakness by augmenting the AGM system with four additional postulates, which are sound relative to a qualitative version of probabilistic conditioning. Finally, we establish a model-based representation theorem which characterizes the augmented system of postulates and constrains, in turns, the way in which entrenchment orderings may be transformed under iterated belief revisions.