Consistency for Partially Defined Constraints

Abstract

Abstract. Partially defined or Open Constraints can be used to model the incomplete knowledge of a concept or a relation. We propose to complete its definition by using Machine Learning techniques. Our technique is composed of two steps: first we learn a classifier for the constraint's projections and then we transform the classifier into a propagator. We show that our technique not only has good learning performances but also yields a very efficient solver for the learned constraint.