Modeling a combinatorial problem is a hard and error-prone task requiring expertise. Constraint acquisition methods can automate this process by learning constraints from examples of solutions and (usually) non-solutions. We describe a new statistical approach based on sequential analysis that is orders of magnitude faster than existing methods, and gives accurate results on popular benchmarks. It is also robust in the sense that it can learn constraints correctly even when the data contain many errors.
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