Learning Implied Global Constraints

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.

• Type: Conference
• Year: 2007
• Where: IJCAI
• Authors: Christian Bessière, Remi Coletta, Thierry Petit
• Keywords: Artificial Intelligence, Constraint Network, Global Constraints, IJCAI 2007, Implied Global Constraints