Learning Implied Global Constraints
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 44-48.
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.
@inproceedings{ijcai2007p5,
title = {Learning Implied Global Constraints},
author = {Bessiere, Christian and Coletta, Remi and Petit, Thierry},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {44--48},
year = {2007},
url = {https://ijcai.org/proceedings/2007/5},
}
TY - CPAPER TI - Learning Implied Global Constraints AU - Bessiere, Christian AU - Coletta, Remi AU - Petit, Thierry T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 44 EP - 48 UR - https://ijcai.org/proceedings/2007/5 ER -Copied
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