Artificial Intelligence
Artificial Intelligence
A knowledge level analysis of belief revision
Proceedings of the first international conference on Principles of knowledge representation and reasoning
Building problem solvers
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Belief liberation (and retraction)
Proceedings of the 9th conference on Theoretical aspects of rationality and knowledge
Knowledge state reconsideration: hindsight belief revision
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Base Belief Change and Optimized Recovery
Proceedings of the 2006 conference on STAIRS 2006: Proceedings of the Third Starting AI Researchers' Symposium
Focused belief revision as a model of fallible relevance-sensitive perception
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
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We define reconsideration, a non-prioritized belief change operation on a finite set of base beliefs. Reconsideration is a hindsight belief change repair that eliminates negative effects caused by the order of previously executed belief change operations. Beliefs that had previously been removed are returned to the base if there no longer are valid reasons for their removal. This might result in less preferred beliefs being removed, and additional beliefs being returned. The end product is an optimization of the belief base, converting the results of a series of revisions to the very base that would have resulted from a batch revision performed after all base beliefs were entered/added. Reconsideration can be done by examining the entire set of all base beliefs (both currently believed and retracted) -- or, if the believed base is consistent, by examining all retracted beliefs for possible return. This, however, is computationally expensive. We present a more efficient, TMS-friendly algorithm, dependency-directed reconsideration (DDR), which can produce the same results by examining only a dynamically determined subset of base beliefs that are actually affected by changes made since the last base optimization process. DDR is an efficient, anytime, belief base optimizing algorithm that eliminates operation order effects.