Towards learning default rules by identifying big-stepped probabilities

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