Finite Domain Constraint Solver Learning

Arnaud Lallouet, Thi-Bich-Hanh Dao, Andrei Legtchenko, AbdelAli Ed-Dbali

Abstract: In this paper, we present an abstract framework for learning a finite domain constraint solver modeled by a set of operators enforcing a consistency. The behavior of the consistency to be learned is taken as the set of examples on which the learning process is applied. The best possible expression of this operator in a given language is then searched. We instantiate this framework to the learning of bound-consistency in the indexical language of Gnu-Prolog.