Almost all k-colorable graphs are easy to color
Journal of Algorithms
Explanation-based learning: a problem solving perspective
Artificial Intelligence
A structural theory of explanation-based learning
A structural theory of explanation-based learning
Constraint satisfaction with a multi-dimensional domain
Proceedings of the first international conference on Artificial intelligence planning systems
Constraint satisfaction using constraint logic programming
Artificial Intelligence - Special volume on constraint-based reasoning
Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
An analysis of learning to plan as a search problem
ML92 Proceedings of the ninth international workshop on Machine learning
A generic arc-consistency algorithm and its specializations
Artificial Intelligence
Statistical Methods for Analyzing Speedup Learning Experiments
Machine Learning
Learning Search Control Knowledge: An Explanation-Based Approach
Learning Search Control Knowledge: An Explanation-Based Approach
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A universal programming language
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Automated synthesis of constrained generators
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Abstraction via approximate symmetry
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 2
An analytic learning system for specializing heuristics
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 2
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This paper describes a set of experiments with a system that synthesizes constraint satisfaction programs. The system, MULTI-TAC, is a CSP "expert" that can specialize a library of generic algorithms and methods for a particular application. MULTI-TAC not only proposes domain-specific versions of its generic heuristics, but also searches for the best combination of these heuristics and integrates them into a complete problem-specific program. We demonstrate MULTI-TAC's capabilities on a combinatorial problem, "Minimum Maximal Matching", and show that MULTI-TAC can synthesize programs for this problem that are on par with hand-coded programs. In synthesizing a program, MULTI-TAC bases its choice of heuristics on the instance distribution, and we show that this capability has a significant impact on the results.