{"id":"W4401459041","doi":"10.1080/03155986.2024.2376446","title":"A multi-agent learning framework for mixed-integer linear programming","year":2024,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Integer programming; Computer science; Linear programming; Branch and price; Integer (computer science); Mathematical optimization; Mathematics; Algorithm; Programming language","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001344942,0.0001134306,0.0001158497,0.0003586654,0.0003258768,0.001246296,0.00008499633,0.0001379632,0.00001844096],"category_scores_gemma":[0.0005024932,0.00009824419,0.00004227395,0.0003559492,0.00003610661,0.001072502,0.0000288441,0.0004234562,0.0001797297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008756953,"about_ca_system_score_gemma":0.00009593187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001905248,"about_ca_topic_score_gemma":0.000001509595,"domain_scores_codex":[0.9987169,0.00003121582,0.0004217168,0.0001021764,0.0004562417,0.0002717373],"domain_scores_gemma":[0.9988837,0.0003674115,0.00001841139,0.00008460849,0.0005396334,0.000106277],"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":[0.00001073373,0.000007038539,0.00006883444,0.0008016962,0.00005955764,9.22871e-7,0.002977825,0.904171,0.00001500892,0.0606602,0.001174479,0.03005268],"study_design_scores_gemma":[0.000137555,0.00003189104,0.00001888712,0.000166561,0.000002264212,0.000008017673,0.001448765,0.750803,0.00003932258,0.00001751936,0.2472351,0.00009114225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001816832,0.0009435333,0.9944904,0.0001308516,0.0008711358,0.00073955,0.00003131373,0.0003571563,0.00061919],"genre_scores_gemma":[0.6797847,0.0002444672,0.315991,0.00006433179,0.0007132366,0.001258411,0.0004731305,0.00004713766,0.001423667],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6784995,"threshold_uncertainty_score":0.9997905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05547316307434643,"score_gpt":0.358079676329731,"score_spread":0.3026065132553846,"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."}}