A General Framework for Revising Belief Bases Using Qualitative Jeffrey's Rule

Authors:
Salem Benferhat;Didier Dubois;Henri Prade;Mary-Anne Williams
Affiliations:
CRIL-CNRS, UMR 8188, Faculté Jean Perrin, Université d'Artois, Lens, France 62307;IRIT, Université Paul Sabatier, Toulouse cedex 09, France 31062;IRIT, Université Paul Sabatier, Toulouse cedex 09, France 31062;Innovation and Enterprise Research Laboratory, University of Technology, Sydney, Australia NSW 2007
Venue:
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Year:
2009

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Abstract

Intelligent agents require methods to revise their epistemic state as they acquire new information. Jeffrey's rule, which extends conditioning to uncertain inputs, is currently used for revising probabilistic epistemic states when new information is uncertain. This paper analyses the expressive power of two possibilistic counterparts of Jeffrey's rule for modeling belief revision in intelligent agents. We show that this rule can be used to recover most of the existing approaches proposed in knowledge base revision, such as adjustment, natural belief revision, drastic belief revision, revision of an epistemic by another epistemic state. In addition, we also show that that some recent forms of revision, namely improvement operators, can also be recovered in our framework.