Peer-Reviewed Journal Details
Mandatory Fields
Prestwich, Steven D.; Freuder, Eugene C.; O'Sullivan, Barry; Browne, David
Annals of Mathematics and Artificial Intelligence
Classifier-based constraint acquisition
Optional Fields
Constraint acquisition Classifier Bayesian Boolean satisfiability
Modeling a combinatorial problem is a hard and error-prone task requiring significant expertise. Constraint acquisition methods attempt to automate this process by learning constraints from examples of solutions and (usually) non-solutions. Active methods query an oracle while passive methods do not. We propose a known but not widely-used application of machine learning to constraint acquisition: training a classifier to discriminate between solutions and non-solutions, then deriving a constraint model from the trained classifier. We discuss a wide range of possible new acquisition methods with useful properties inherited from classifiers. We also show the potential of this approach using a Naive Bayes classifier, obtaining a new passive acquisition algorithm that is considerably faster than existing methods, scalable to large constraint sets, and robust under errors.
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