Dual viewpoint heuristics for binary constraint satisfaction problems
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Exploiting the deep structure of constraint problems
Artificial Intelligence
The OPL optimization programming language
The OPL optimization programming language
Compiling High-Level Type Constructors in Constraint Programming
PADL '01 Proceedings of the Third International Symposium on Practical Aspects of Declarative Languages
An Open-Ended Finite Domain Constraint Solver
PLILP '97 Proceedings of the9th International Symposium on Programming Languages: Implementations, Logics, and Programs: Including a Special Trach on Declarative Programming Languages in Education
Schema-Guided Synthesis of Constraint Logic Programs
ASE '98 Proceedings of the 13th IEEE international conference on Automated software engineering
Adaptive problem-solving for large-scale scheduling problems: a case study
Journal of Artificial Intelligence Research
Compiling High-Level Type Constructors in Constraint Programming
PADL '01 Proceedings of the Third International Symposium on Practical Aspects of Declarative Languages
Towards Inferring Labelling Heuristics for CSP Application Domains
KI '01 Proceedings of the Joint German/Austrian Conference on AI: Advances in Artificial Intelligence
An approach for dynamic split strategies in constraint solving
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Adaptive enumeration strategies and metabacktracks for constraint solving
ADVIS'06 Proceedings of the 4th international conference on Advances in Information Systems
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In constraint solvers, variable and value ordering heuristics are used to finetune the performance of the underlying search and propagation algorithms. However, few guidelines have been proposed for when to choose what heuristic among the wealth of existing ones. Empirical studies have established that this would be very hard, as none of these heuristics outperforms all the other ones on all instances of all problems (for an otherwise fixed solver). The best heuristic varies not only between problems, but even between different instances of the same problem. Taking heed of the popular dictum "If you can't beat them, join them!" we devise a practical meta-heuristic that automatically chooses, at run-time, the "best" available heuristic for the instance at hand. It is applicable to an entire class of NP-complete subset problems.