An inductive logic programming framework to learn a concept from ambiguous examples

Abstract

We address a learning problem with the following peculiarity: we search for characteristic features common to a learning set of objects related to a target concept. In particular we approach the cases where descriptions of objects are ambiguous: they represent several incompatible realities. Ambiguity arises because each description only contains indirect information from which assumptions can be derived about the object. We suppose here that a set of constraints allows the identification of "coherent" sub-descriptions inside each object. We formally study this problem, using an Inductive Logic Programming framework close to characteristic induction from interpretations. In particular, we exhibit conditions which allow a pruned search of the space of concepts. Additionally we propose a method in which a set of hypothetical examples is explicitly calculated for each object prior to learning. The method is used with promising results to search for secondary substructures common to a set of RNA sequences.