The weighted majority algorithm
Information and Computation
Constraint-based reasoning
Methods for combining experts' probability assessments
Neural Computation
Proverb: the probabilistic cruciverbalist
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
A Sufficient Condition for Backtrack-Free Search
Journal of the ACM (JACM)
Guest Editors' Introduction: Constraints
IEEE Intelligent Systems
A Meta-Heuristic Factory for Vehicle Routing Problems
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
The Brélaz Heuristic and Optimal Static Orderings
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
Towards Inferring Labelling Heuristics for CSP Application Domains
KI '01 Proceedings of the Joint German/Austrian Conference on AI: Advances in Artificial Intelligence
On heuristic reasoning, reactivity, and search
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
On forward checking for non-binary constraint satisfaction
Artificial Intelligence
The Adaptive Constraint Engine
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
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Although constraint programming offers a wealth of strong, generalpurpose methods, in practice a complex, real application demands a person who selects, combines, and refines various available techniques for constraint satisfaction and optimization. Although such tuning produces efficient code, the scarcity of human experts slows commercialization. The necessary expertise is of two forms: constraint programming expertise and problem-domain expertise. The former is in short supply, and even experts can be reduced to trial and error prototyping; the latter is difficult to extract. The project described here seeks to automate both the application of constraint programming expertise and the extraction of domain-specific expertise. It applies FORR, an architecture for learning and problem-solving, to constraint solving. FORR develops expertise from multiple heuristics. A successful case study is presented on coloring problems.