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

  • Salem Benferhat
  • Didier Dubois
  • Henri Prade
  • Mary-Anne Williams
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5722)


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.


Intelligent Agent Epistemic State Belief Revision Belief Change Possibility Distribution 
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© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Salem Benferhat
    • 1
  • Didier Dubois
    • 2
  • Henri Prade
    • 2
  • Mary-Anne Williams
    • 3
  1. 1.CRIL-CNRS, UMR 8188, Faculté Jean PerrinUniversité d’ArtoisLensFrance
  2. 2.IRITUniversité Paul SabatierToulouse cedex 09France
  3. 3.Innovation and Enterprise Research LaboratoryUniversity of TechnologySydneyAustralia

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