{"id":"W4402686278","doi":"10.2514/6.2024-4169","title":"Optimizing Airport Ground Movements Using Multi-Agents Reinforcement Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Air Traffic Management and Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Reinforcement learning; Computer science; Artificial intelligence; Human–computer interaction","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0008995347,0.0008667021,0.0009430198,0.0004081559,0.000365395,0.0005436837,0.001040574,0.0008496905,0.001068886],"category_scores_gemma":[0.002281891,0.0004180719,0.0004291105,0.0002785239,0.0007188839,0.0005819841,0.0007725371,0.0009776969,0.0001749219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008387124,"about_ca_system_score_gemma":0.001143954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008625263,"about_ca_topic_score_gemma":0.006842242,"domain_scores_codex":[0.9996562,0.0001279889,0.00001678259,0.00007117032,0.00006779271,0.00006006233],"domain_scores_gemma":[0.9987973,0.000727964,0.0001701677,0.00006183683,0.0001640542,0.00007868515],"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":[0.00001432714,0.00001812774,0.0002702271,0.000009180851,0.000009478516,0.00001684824,0.000008700482,0.9938051,0.0002416892,0.0007721072,0.00007528035,0.004758863],"study_design_scores_gemma":[0.000003319149,0.00001003504,0.00002152901,8.819346e-7,0.000001333887,0.000001712565,0.000001553054,0.9995356,0.00007008747,0.0003137049,0.00003958126,8.054526e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05984669,0.0001543321,0.9372001,0.0001533525,0.00003501957,0.00006852536,0.00003040574,0.0003813013,0.002130325],"genre_scores_gemma":[0.9321594,0.00006513418,0.06643469,0.00005501833,0.00001604705,0.00008515174,0.00004557267,0.00002735759,0.001111706],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008625263,"threshold_uncertainty_score":0.0171501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02867521796675299,"score_gpt":0.2515592276552411,"score_spread":0.2228840096884881,"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."}}