On the consistency of defeasible databases
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
Conditional logics for default reasoning and belief revision
Conditional logics for default reasoning and belief revision
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
Optimal Tableaux for Conditional Logics with Cautious Monotonicity
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Belief revision with uncertain inputs in the possibilistic setting
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
The probability of a possibility: adding uncertainty to default rules
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
On the logic of iterated non-prioritised revision
WCII'02 Proceedings of the 2002 international conference on Conditionals, Information, and Inference
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Both the dynamics of belief change and the process of reasoning by default can be based on the conditional belief set of an agent, represented as a set of "if-then" rules. In this paper we address the open problem of formalizing the dynamics of revising this conditional belief set by new if-then rules, be they interpreted as new default rules or new revision policies. We start by providing a purely semantic characterization, based on the semantics of conditional rules, which induces logical constraints on any such revision process. We then introduce logical (syntax-independent) and syntax-dependent techniques, and provide a precise characterization of the set of conditionals that hold after the revision. In addition to formalizing the dynamics of revising a default knowledge base, this work also provides some of the necessary formal tools for establishing the truth of nested conditionals, and attacking the problem of learning new defaults.