Formulating Template Consistency in Inductive Logic Programming as a Constraint Satisfaction Problem

Inductive Logic Programming (ILP) deals with the problem of finding a hypothesis covering positive examples and excluding negative examples, where both hypotheses and examples are expressed in first-order logic. In this paper we employ constraint satisfaction techniques to model and solve a problem known as template ILP consistency, which assumes that the structure of a hypothesis is known and the task is to find a unification of the contained variables such that no negative example is subsumed by the hypothesis and all positive examples are subsumed.