A simulated annealing framework for ILP

Abstract

In Inductive Logic Programming (ILP), algorithms which are purely of the bottom-up or top-down type encounter several problems in practice. Since a majority of them are greedy ones, these algorithms find clauses in local optima, according to the "quality" measure used for evaluating the results. Moreover, when learning clauses one by one, induced clauses become less interesting to cover few remaining examples. In this paper, we propose a simulated annealing framework to overcome these problems. Using a refinement operator, we define neighborhood relations on clauses and on hypotheses (i.e. sets of clauses). With these relations and appropriate quality measures, we show how to induce clauses (in a coverage approach), or to induce hypotheses directly by using simulated annealing algorithms. We discuss the necessary conditions on the refinement operators and the evaluation measures in order to increase the algorithm's effectivity. Implementations are described and experimentation results are presented. © Springer-Verlag Berlin Heidelberg 2004.