{"data":{"event":{"id":"86d77a9b-d047-490f-97ed-7b20e77b0627","slug":"learning-symbolic-constraint-representations-from-examples-a-neuro-symbo-1e755da9a8","title":"Learning Symbolic Constraint Representations from Examples: A Neuro-Symbolic Approach","short_summary":"arXiv:2609.12267v1 Announce Type: new \nAbstract: 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 this paper, we propose a neuro-symbolic framework for automatic CA that significantly reduces user involvement by introducing neural Oracle Transformer models which learn to emulate user responses and to generalize conceptual knowledge. 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