A big-stepped probability approach for discovering default rules

Salem Benferhat (1) , Didier Dubois (1) , Sylvain Lagrue (1) , Henri Prade (1)

1 IRIT-ADRIA - Argumentation, Décision, Raisonnement, Incertitude et Apprentissage

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", which are known to provide a semantics for non-monotonic reasoning. The rules which are learnt are genuine default rules, which could be used (under some conditions) in a non-monotonic reasoning system and can be encoded in possibilistic logic.

Keywords: knowledge discovery; default rules; non-monotonic reasoning; big stepped probabilities; possibility theory

Domains: Computer Science [cs]; Artificial Intelligence [cs.AI]

Salem Benferhat, Didier Dubois, Sylvain Lagrue, Henri Prade. A big-stepped probability approach for discovering default rules. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2003, 11 (sppl. 01), pp.1-14. ⟨10.1142/S0218488503002235⟩. ⟨hal-03299557⟩