On Learning Constraint Problems

Arnaud Lallouet (1) , Matthieu Lopez (2) , Lionel Martin (2) , Christel Vrain (2)

1 Equipe CODAG - Laboratoire GREYC - UMR6072
2 LIFO - Laboratoire d'Informatique Fondamentale d'Orléans

It is well known that modeling with constraints networks require a fair expertise. Thus tools able to automatically generate such networks have gained a major interest. The major contribution of this paper is to set a new framework based on Inductive Logic Programming able to build a constraint model from solutions and non-solutions of related problems. The model is expressed in a middle-level modeling language. On this particular relational learning problem, traditional topdown search methods fall into blind search and bottom-up search methods produce too expensive coverage tests. Recent works in Inductive Logic Programming about phase transition and crossing plateau shows that no general solution can face all these difficulties. In this context, we have designed an algorithm combining the major qualities of these two types of search techniques.We present experimental results on some benchmarks ranging from puzzles to scheduling problems.

Domaines: Intelligence artificielle [cs.AI]; Traitement du texte et du document

Arnaud Lallouet, Matthieu Lopez, Lionel Martin, Christel Vrain. On Learning Constraint Problems. International Conference on Tools with Artificial Intelligence,ICTAI, Oct 2010, Arras, France. pp.45-52. ⟨hal-01016891⟩