ML Anthology

Learning Implied Global Constraints

Christian Bessiere, Remi Coletta, Thierry Petit
IJCAI 2007 pp. 44-49
/ijcai/2007/bessiere2007ijcai-learning/

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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Text

Bessiere et al. "Learning Implied Global Constraints." International Joint Conference on Artificial Intelligence, 2007.

Markdown

[Bessiere et al. "Learning Implied Global Constraints." International Joint Conference on Artificial Intelligence, 2007.](https://mlanthology.org/ijcai/2007/bessiere2007ijcai-learning/)

BibTeX

@inproceedings{bessiere2007ijcai-learning,
  title     = {{Learning Implied Global Constraints}},
  author    = {Bessiere, Christian and Coletta, Remi and Petit, Thierry},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2007},
  pages     = {44-49},
  url       = {https://mlanthology.org/ijcai/2007/bessiere2007ijcai-learning/}
}