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

Authors:
Salem Benferhat;Didier Dubois;Henri Prade;Mary-Anne Annewilliams
Affiliations:
CRIL-CNRS, UMR 8188, Faculté Jean Perrin, Université d'Artois, rue Jean Souvraz, 62307 Lens, France. E-mail: benferhat@cril.univ-artois.fr;IRIT - Université Paul Sabatier, 118 route de Narbonne 31062 Toulouse cedex 09, France;IRIT - Université Paul Sabatier, 118 route de Narbonne 31062 Toulouse cedex 09, France;Innovation and Enterprise Research Laboratory, University of Technology, Sydney NSW 2007, Australia
Venue:
Fundamenta Informaticae - Methodologies for Intelligent Systems
Year:
2010

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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 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.