Nonmonotonic reasoning, preferential models and cumulative logics
Artificial Intelligence
Propositional knowledge base revision and minimal change
Artificial Intelligence
What does a conditional knowledge base entail?
Artificial Intelligence
Conditional entailment: bridging two approaches to default reasoning
Artificial Intelligence
Unifying default reasoning and belief revision in a modal framework
Artificial Intelligence
Conditional logics of normality: a modal approach
Artificial Intelligence
On the logic of iterated belief revision
Artificial Intelligence
Explanations, belief revision and defeasible reasoning
Artificial Intelligence
Dynamic belief revision operators
Artificial Intelligence
A consistency-based approach for belief change
Artificial Intelligence
Weakening conflicting information for iterated revision and knowledge integration
Artificial Intelligence - Special issue on logical formalizations and commonsense reasoning
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
The Limit Assumption and Multiple Revision
Journal of Logic and Computation
Iterated belief revision, revised
Artificial Intelligence
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Admissible and restrained revision
Journal of Artificial Intelligence Research
Revision sequences and nested conditionals
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Inconsistency management and prioritized syntax-based entailment
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Representation theorems for multiple belief changes
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Reasoning under inconsistency: the forgotten connective
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
A maximum entropy approach to nonmonotonic reasoning
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
An argumentation-based approach to cooperative multi-source epistemic conflict resolution
MATES'12 Proceedings of the 10th German conference on Multiagent System Technologies
Axiomatic characterization of belief merging by negotiation
Multimedia Tools and Applications
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The area of belief revision studies how a rational agent may incorporate new information about a domain into its belief corpus. An agent is characterised by a belief state K, and receives a new item of information @a which is to be included among its set of beliefs. Revision then is a function from a belief state and a formula to a new belief state. We propose here a more general framework for belief revision, in which revision is a function from a belief state and a finite set of formulas to a new belief state. In particular, we distinguish revision by the set {@a,@b} from the set {@a@?@b}. This seemingly innocuous change has significant ramifications with respect to iterated belief revision. A problem in approaches to iterated belief revision is that, after first revising by a formula and then by a formula that is inconsistent with the first formula, all information in the original formula is lost. This problem is avoided here in that, in revising by a set of formulas S, the resulting belief state contains not just the information that members of S are believed to be true, but also the counterfactual supposition that if some members of S were later believed to be false, then the remaining members would nonetheless still be believed to be true. Thus if some members of S were in fact later believed to be false, then the other elements of S would still be believed to be true. Hence, we provide a more nuanced approach to belief revision. The general approach, which we call parallel belief revision, is independent of extant approaches to iterated revision. We present first a basic approach to parallel belief revision. Following this we combine the basic approach with an approach due to Jin and Thielscher for iterated revision. Postulates and semantic conditions characterising these approaches are given, and representation results provided. We conclude with a discussion of the possible ramifications of this approach in belief revision in general.