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FUIN
2010

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

13 years 11 months ago
A Framework for Iterated Belief Revision Using Possibilistic Counterparts to Jeffrey's Rule
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.
Salem Benferhat, Didier Dubois, Henri Prade, Mary-
Added 25 Jan 2011
Updated 25 Jan 2011
Type Journal
Year 2010
Where FUIN
Authors Salem Benferhat, Didier Dubois, Henri Prade, Mary-Anne Williams
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