Learning Structured Constraint Models: a First Attempt
Résumé
In this paper we give an overview of a novel tool which learns structured constraint models from flat, positive examples of solutions. It is based on previous work on a Constraint Seeker, which finds constraints in the global constraint catalog satisfying positive and negative examples. In the current tool we extend this system to find structured conjunctions of constraints on regular subsets of variables in the given solutions. Two main elements of the approach are a bi-criteria optimization problem which finds conjunctions of constraints which are both regular and relevant, and a syntactic dominance check between conjunctions, which removes implied constraints without requiring a full theorem prover, using meta-data in the constraint catalog. Some initial experiments on a proof-of-concept implementation show promising results.