Machine Learning-Based Generalization Queries for Constraint Acquisition
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I presented our work on machine learning-based generalization queries for interactive constraint acquisition at the ModRef 2026 workshop, held with CP 2026 as part of FLoC 2026. Standard interactive CA learns one ground constraint at a time, even when many constraints share the same underlying pattern. We propose using ML to generate generalization queries that capture problem-level constraint patterns, aiming to reduce the number of queries needed.