On avoiding redundancy in inductive logic programming

Abstract

ILP systems induce rst-order clausal theories performing a search through very large hypotheses spaces containing redundant hypotheses. The generation of redundant hypotheses may prevent the systems from nding good models and increases the time to induce them. In this paper we propose a classication of hypotheses redundancy and show how expert knowledge can be provided to an ILP system to avoid it. Experimental results show that the number of hypotheses generated and execution time are reduced when expert knowledge is used to avoid redundancy.