A Framework for Iterated Belief Revision Using Possibilistic Counterparts to Jeffrey's Rule

• Autorzy: Benferhat S. , Dubois D. , Prade H. , Williams M-A.
• Języki publikacji: EN

Abstrakty

EN

Intelligent agents require methods to revise their epistemic state as they acquire new information. Jeffrey’s rule, which extends conditioning to probabilistic inputs, is appropriate for revising probabilistic epistemic states when new information comes in the form of a partition of events with new probabilities and has priority over prior beliefs. This paper analyses the expressive power of two possibilistic counterparts to Jeffrey's rule for modeling belief revision in intelligent agents. We show that this rule can be used to recover several existing approaches proposed in knowledge base revision, such as adjustment, natural belief revision, drastic belief revision, and the revision of an epistemic state by another epistemic state. In addition, we also show that some recent forms of revision, called improvement operators, can also be recovered in our framework.

Słowa kluczowe

EN

belief revision; Jeffrey's rule; possibility theory

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2010
• Tom: Vol. 99, nr 2
• Strony: 147--168
• Opis fizyczny: Bibliogr. 33 poz., tab.

Twórcy

autor

Benferhat S.

autor

Dubois D.

autor

Prade H.

autor

Williams M-A.

• CRIL-CNRS, UMR 8188, Faculté Jean Perrin, Université d’Artois, rue Jean Souvraz, 62307 Lens, France,

Bibliografia

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