Learning to Learn in Interactive Constraint Acquisition
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Our paper on “Learning to Learn in Interactive Constraint Acquisition” was presented in AAAI24. In Constraint Acquisition (CA), the goal is to assist the user by automatically learning the model. In (inter)active CA, this is done by interactively posting queries to the user, e.g., asking whether a partial solution satisfies their (unspecified) constraints or not. In our proposed approach, the system actually learns (implicitely) the structure of the problem, using classifiers to learn the existing patterns, using a feature representation of the constraints. The query-based learning is making this explicit, confirming it through queries to the user. This resulted in up to 70% reduction in the number of queries needed to learn the correct set of constraints!