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Learning Symbolic Constraint Representations from Examples: A Neuro-Symbolic Approach
Learning user-defined concepts as constraint networks has been extensively studied in the constraint acquisition (CA) literature. However, existing approaches typically rely on intensive interactions with a human oracle, making the learning process costly in terms of time and number of queries. In t
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