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Learning Implied Global Constraints

Christian Bessière 1 Remi Coletta 2, 3 Thierry Petit 4
1 COCONUT - Agents, Apprentissage, Contraintes
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
2 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : 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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Contributor : Martine Peridier <>
Submitted on : Tuesday, December 11, 2007 - 4:23:05 PM
Last modification on : Thursday, May 14, 2020 - 6:58:06 PM


  • HAL Id : lirmm-00195896, version 1


Christian Bessière, Remi Coletta, Thierry Petit. Learning Implied Global Constraints. IJCAI'07: International Joint Conference on Artificial Intelligence, 2007, Hyderabad, India. pp.50-55. ⟨lirmm-00195896⟩



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