Learning Linear Constraints in Inductive Logic Programming

Abstract

In this paper, we present a system, called icc , that learns constrained logic programs containing function symbols. The particularity of our approach is to consider, as in the field of Constraint Logic Programming, a specific computation domain and to handle terms by taking into account their values in this domain. Nevertheless, an earlier version of our system was only able to learn constraints X _i=t, where X _i is a variable and t is a term. We propose here a method for learning linear constraints. It has already been a lot studied in the field of Statistical Learning Theory and for learning Oblic Decision Trees. As far as we know, the originality of our approach is to rely on a Linear Programming solver. Moreover, integrating it in icc enables to learn non linear constraints.