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

Intelligent agents require methods to revise their epistemic state as they acquire new information. Jeffrey’s rule, which extends conditioning to uncertain inputs, is used to revise 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 reinforcement operators, can also be recovered in our framework.

• Type: Conference
• Year: 2009
• Where: ISMIS
• Authors: Salem Benferhat, Didier Dubois, Henri Prade, Mary-Anne Williams
• Keywords: Artificial Intelligence, Belief Revision, Epistemic State, ISMIS 2009, Jeffrey’s Rule