Machine Learning Based Recommendation Queries for Constraint Acquisition

Active constraint acquisition aids users in specifying problem constraints by enabling queries that facilitate convergence on a unique target constraint network. Although various algorithms have been proposed to reduce the number of queries needed for convergence, few utilize the problem's structure. In this paper, we propose leveraging the problem's structure by transforming the partially learned constraint network into features vectors used as inputs for machine learning models. These models are trained to predict missing constraints, which are then proposed to the user via recommendation queries. Experimental results demonstrate significant performance improvements over existing approaches.