Affiliations: CRIL-CNRS, UMR 8188, Faculté Jean Perrin,
Université d'Artois, rue Jean Souvraz, 62307 Lens, France. E-mail:
[email protected] | 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
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.
Keywords: Belief revision, Jeffrey's rule, possibility theory
