Redundant Covering with Global Evaluation in the RC1 Inductive Learner

Abstract

This paper presents an inductive method that learns a logic program represented as an ordered list of clauses. The input consists of a training set of positive examples and background knowledge represented intensionally as a logic program. Our method starts by constructing the explanations of all the positive examples in terms of background knowledge, linking the input to the output arguments. These are used as candidate hypotheses and organized, by relation of generality, into a set of hierarchies (forest). In the second step the candidate hypotheses are analysed with the aim of establishing their effective coverage. In the third step all the inconsistencies are evaluated. This analysis permits to add, at each step, the best hypothesis to the theory. The method was applied to learn the past tense of English verbs. The method presented achieves more accurate results than the previous work by Mooney and Califf [7].