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Towards learning default rules by identifying big-stepped probabilities

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4 Author(s)
Benferhat, S. ; Inst. de Recherche en Inf. de Toulouse, France ; Dubois, D. ; Lagrue, S. ; Prade, H.

This paper deals with the extraction of default rules from a database of examples. The proposed approach is based on a special kind of probability distributions, called "big-stepped probabilities". It has been shown that these distributions provide a semantics for the System P developed by Kraus, Lehmann et Magidor for representing non-monotonic consequence relations. Thus the rules which are learnt are genuine default rules, which could be used (under some conditions) in a nonmonotonic reasoning system, which can be encoded in possibilistic logic

Published in:

IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th  (Volume:3 )

Date of Conference:

25-28 July 2001