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| HAL : lirmm-00195896, version 1 |
| Fiche détaillée |  Récupérer au format |
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| IJCAI'07: International Joint Conference on Artificial Intelligence, Hyderabad, India : |
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| Learning Implied Global Constraints |
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| Christian Bessiere 1Remi Coletta 2 |
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| (2007) |
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| Finding a constraint network that will be efficiently solved by a constraint solver requires a strong expertise in Constraint Programming. Hence, there is an increasing interest in automatic reformulation. This paper presents a general framework for learning implied global constraints in a constraint network assumed to be provided by a non-expert user. The learned global constraints can then be added to the network to improve the solving process. We apply our technique to global cardinality constraints. Experiments show the significance of the approach. |
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| 1 : | Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM) |
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CNRS : UMR5506 – Université Montpellier II - Sciences et techniques |
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| 2 : | LRD |
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LRD |
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| 3 : | Laboratoire d'Informatique de Nantes Atlantique (LINA) |
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CNRS : FRE2729 – Université de Nantes – École Nationale Supérieure des Mines - Nantes |
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| INFO/COCONUT INFO/ZENITH |
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| Domaine | : | Sciences de l'ingénieur/Autre |
| lirmm-00195896, version 1 | |
| http://hal-lirmm.ccsd.cnrs.fr/lirmm-00195896 | |
| oai:hal-lirmm.ccsd.cnrs.fr:lirmm-00195896 | |
| Contributeur : Martine Peridier | |
| Soumis le : Mardi 11 Décembre 2007, 16:23:05 | |
| Dernière modification le : Mardi 26 Février 2008, 11:52:02 | |
