{"id":"W7117896362","doi":"10.1613/jair.1.19533","title":"Combining Constraint Programming and Machine Learning: From Current Progress to Future Opportunities","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Research","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Constraint programming; Key (lock); Resource (disambiguation); Constraint (computer-aided design); Resource constraints; Open research","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001864747,0.0001054459,0.000194803,0.0006714295,0.000319052,0.0007417439,0.0004393596,0.0000554832,0.00003947003],"category_scores_gemma":[0.0002745558,0.00009237569,0.00005432537,0.000713717,0.0002631955,0.0004148059,0.0002383753,0.0008203943,0.000005957297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007322568,"about_ca_system_score_gemma":0.0004932189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002618588,"about_ca_topic_score_gemma":0.0000366164,"domain_scores_codex":[0.9980698,0.0003063596,0.0005382764,0.0002142402,0.0005741988,0.0002970954],"domain_scores_gemma":[0.9982632,0.0003273282,0.0001490397,0.0001489954,0.0008957188,0.0002156897],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003226189,0.00006028916,0.001014078,0.00001342056,0.00001870342,0.00002366155,0.001832789,0.0003852337,0.0001610498,0.08518232,0.00005763488,0.9112186],"study_design_scores_gemma":[0.0005116388,0.002743919,0.004029565,0.002393421,0.00006314746,0.0002399919,0.09036747,0.5655444,0.02445015,0.1119909,0.1965873,0.001078091],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01966806,0.001733952,0.9655684,0.01166124,0.0008523735,0.0002487429,0.000001544629,0.0000350886,0.0002306267],"genre_scores_gemma":[0.9651421,0.0006344991,0.0339281,0.00004734585,0.0001943027,0.000008226514,0.000001725241,0.000005665416,0.00003803219],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.945474,"threshold_uncertainty_score":0.7152654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2147351455846759,"score_gpt":0.4125603237854467,"score_spread":0.1978251782007708,"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."}}