Semi-Automatic Modeling by Constraint Acquisition

Abstract : Constraint programming is a technology which is now widely used to solve combinatorial problems in industrial applications. However, using it requires considerable knowledge and expertise in the field of constraint reasoning. This paper introduces a framework for automatically learning constraint networks from sets of instances that are either acceptable solutions or non-desirable assignments of the problem we would like to express. Such an approach has the potential to be of assistance to a novice who is trying to articulate her constraints. By restricting the language of constraints used to build the network, this could also assist an expert to develop an efficient model of a given problem.
Type de document :
Communication dans un congrès
Rossi F. CP: Constraint Programming, Sep 2003, Kinsale, Ireland. Springer, 9th International Conference on Principles and Practice of Constraint Programming, LNCS (2833), pp.812-816, 2003, Principles and Practice of Constraint Programming – CP 2003. 〈10.1007/978-3-540-45193-8_58〉
Liste complète des métadonnées

https://hal-lirmm.ccsd.cnrs.fr/lirmm-00269537
Contributeur : Christine Carvalho de Matos <>
Soumis le : jeudi 3 avril 2008 - 08:21:44
Dernière modification le : jeudi 24 mai 2018 - 15:59:23

Lien texte intégral

Identifiants

Collections

Citation

Remi Coletta, Christian Bessière, Barry O'Sullivan, Eugene C. Freuder, Sarah O'Connell, et al.. Semi-Automatic Modeling by Constraint Acquisition. Rossi F. CP: Constraint Programming, Sep 2003, Kinsale, Ireland. Springer, 9th International Conference on Principles and Practice of Constraint Programming, LNCS (2833), pp.812-816, 2003, Principles and Practice of Constraint Programming – CP 2003. 〈10.1007/978-3-540-45193-8_58〉. 〈lirmm-00269537〉

Partager

Métriques

Consultations de la notice

53