Learning Extended Logic Programs

Abstract

This paper presents a method to generate nonmonotonic rules with exceptions from positive/negative examples and background knowledge in Inductive Logic Programming. We adopt extended logic programs as the form of programs to be learned, where two kinds of negation-negation as failure and classical negation-are effectively used in the presence of incomplete information. While default rules are generated as specialization of general rules that cover positive examples, exceptions to general rules are identified from negative examples and are then generalized to rules for cancellation of defaults. We implemented the learning system LELP based on the proposed method. In LELP, when the numbers of positive and negative examples are very close, either parallel default rules with positive and negative consequents or nondeterministic rules are learned. Moreover, hierarchical defaults can also be learned by recursively calling the exception identification algorithm.