Abstract
Multi-tac is a learning system that synthesizes heuristic constraint satisfaction programs. The system takes a library of generic algorithms and heuristics and specializes them for a particular application. We present a detailed case study with three different distributions of a single combinatorial problem, “Minimum Maximal Matching”, and show that Muti-tac can synthesize programs for these different distributions that perform on par with hand-coded programs and that exceed the performance of some well-known satisfiability algorithms. In synthesizing a program, Multi-tac bases its choice of heuristics on an instance distribution, and we demonstrate that this capability has a significant impact on the results.
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Minton, S. Automatically configuring constraint satisfaction programs: A case study. Constraints 1, 7–43 (1996). https://doi.org/10.1007/BF00143877
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DOI: https://doi.org/10.1007/BF00143877