Learning Implied Global Constraints
نویسندگان
چکیده
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.
منابع مشابه
Acquiring Parameters of Implied Global Constraints
This paper presents a technique for learning parameterized implied constraints. They can be added to a model to improve the solving process. Experiments on implied Gcc constraints show the interest of our approach.
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