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.
https://hal-lirmm.ccsd.cnrs.fr/lirmm-00195896
Contributor : Martine Peridier
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Submitted on : Tuesday, December 11, 2007 - 4:23:05 PM
Last modification on : Tuesday, June 19, 2018 - 1:21:08 AM
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⟩
