{"id":"W2626637010","doi":"10.4230/lipics.cp.2023.25","title":"Learning a Generic Value-Selection Heuristic Inside a Constraint Programming Solver","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":523,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Decomposition; Value (mathematics); Computer science; Artificial intelligence; Machine learning; Chemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002489097,0.001231581,0.001238604,0.0006683621,0.0004200772,0.001479503,0.001951388,0.002238475,0.005142099],"category_scores_gemma":[0.00906559,0.0006081467,0.00089055,0.0009063312,0.001531945,0.001658173,0.00188331,0.002551159,0.0008429335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001208169,"about_ca_system_score_gemma":0.002315177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001877745,"about_ca_topic_score_gemma":0.002511876,"domain_scores_codex":[0.9986004,0.0005695784,0.00007332216,0.000329362,0.0002360361,0.0001912747],"domain_scores_gemma":[0.9962202,0.002722851,0.000304952,0.0002913158,0.0002967469,0.0001639758],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001120283,0.0001332521,0.0008551473,0.0001855505,0.00005122298,0.0001335652,0.00009790236,0.8495513,0.002104193,0.06050227,0.003111804,0.08316179],"study_design_scores_gemma":[0.00002015188,0.00002264751,0.00004378961,0.00001417409,0.000006330248,0.0000149179,0.000008780971,0.9827047,0.0006053961,0.01596327,0.0005905093,0.000005412751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009355835,0.00009579103,0.9866548,0.0002610932,0.00003079299,0.00008565192,0.00004767465,0.0004108635,0.003057518],"genre_scores_gemma":[0.3869174,0.0001884365,0.6085796,0.000396389,0.00006947942,0.000410825,0.0002127166,0.0002386689,0.002986515],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005142099,"threshold_uncertainty_score":0.01720202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07875522306874225,"score_gpt":0.2092543842109096,"score_spread":0.1304991611421674,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}