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

Publisher:
Springer
Publication Type:
Conference Proceeding
Citation:
Foundations of Intelligent Systems - 18th International Symposium, ISMIS 2009: Lecture Notes in Artificial Intelligence vol 5722, 2009, pp. 612 - 621
Issue Date:
2009-01
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Intelligent agents require methods to revise their epistemic state as they acquire new information. Jeffreys 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 Jeffreys 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.
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