{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001279635,0.0001614386,0.0001020877,0.0001585916,0.0000940082,0.0001663034,0.00008698046,0.00004801166,0.0006419916],"category_scores_gemma":[0.00000399456,0.0001631164,0.00005372316,0.0002121081,0.000009506477,0.0003774024,0.00005248202,0.0001325499,0.00008984225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001215366,"about_ca_system_score_gemma":0.000007324558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001938611,"about_ca_topic_score_gemma":0.000003215622,"domain_scores_codex":[0.9991298,0.000007517323,0.0002378136,0.000184233,0.0001871193,0.0002534554],"domain_scores_gemma":[0.9998015,0.000008763562,0.00001593451,0.0001075462,0.00001522157,0.00005102218],"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":[0.000001013594,0.000006385816,0.0001103811,0.0001178265,0.00008895234,0.00001310433,0.0003188991,0.9950907,0.0001423218,0.0002883348,0.0004328183,0.003389315],"study_design_scores_gemma":[0.0001902994,0.00001156476,0.00007644838,0.00007189463,0.00002557612,9.015999e-7,0.000190548,0.988217,0.0001366314,0.000003807235,0.01088679,0.0001885696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01732274,0.0002911775,0.9509299,0.000009032146,0.0007119852,0.000220202,2.858141e-7,0.001363617,0.02915102],"genre_scores_gemma":[0.9541255,0.0002081395,0.03173962,0.00004843584,0.00008370752,0.00001159571,0.00004549547,0.00005875102,0.01367879],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9368027,"threshold_uncertainty_score":0.7029358,"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."}}