{"id":"W3124755563","doi":"10.1109/jiot.2021.3054649","title":"Leveraging Multiagent Learning for Automated Vehicles Scheduling at Nonsignalized Intersections","year":2021,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Traffic control and management","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Reinforcement learning; Computer science; Intersection (aeronautics); Scheduling (production processes); Distributed computing; Artificial neural network; Artificial intelligence; Multi-agent system; Intelligent transportation system; Real-time computing; Mathematical optimization; Engineering; Transport engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005257106,0.0005452083,0.0005025209,0.0002535093,0.0004392398,0.0006192792,0.00103998,0.0004995355,0.0008203678],"category_scores_gemma":[0.001169927,0.000255404,0.000290698,0.0002047123,0.000508756,0.0007366872,0.0008660165,0.0007896842,0.0001709539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006279683,"about_ca_system_score_gemma":0.001117068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006613924,"about_ca_topic_score_gemma":0.006021335,"domain_scores_codex":[0.9996731,0.00007316442,0.00001584139,0.0000916283,0.00006842932,0.00007782914],"domain_scores_gemma":[0.9995244,0.0001507746,0.000109227,0.00004895546,0.0001013925,0.00006531177],"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":[0.00006282929,0.00006618646,0.0009646296,0.00001860058,0.00002213111,0.00005133429,0.00004525032,0.9670957,0.002373358,0.003078339,0.0003542019,0.02586738],"study_design_scores_gemma":[0.000003092631,0.00001471975,0.00005420926,6.804209e-7,0.000002294378,0.000002569048,0.000003349174,0.9990408,0.0002352211,0.0005525435,0.00008911143,0.000001358669],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1019714,0.0002646754,0.8936131,0.0001894263,0.0000599623,0.00004049452,0.00002237322,0.0005737858,0.003264847],"genre_scores_gemma":[0.9809894,0.00004231254,0.01810194,0.00003781614,0.00001316301,0.00002586479,0.00001940545,0.00001193794,0.00075816],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006613924,"threshold_uncertainty_score":0.01315081,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01589078148499553,"score_gpt":0.2381396921188346,"score_spread":0.2222489106338391,"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."}}