{"id":"W2097152070","doi":"10.1109/itsc.2007.4357719","title":"Urban Traffic Control Based on Learning Agents","year":2007,"lang":"en","type":"article","venue":"","topic":"Traffic control and management","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Traffic flow (computer networking); Controller (irrigation); Control (management); Traffic signal; Intelligent transportation system; Reinforcement learning; Distributed computing; Artificial intelligence; Real-time computing; Computer network; Engineering; Transport engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005457989,0.0003787023,0.0004604926,0.0002827996,0.0004497737,0.0007714733,0.0007187,0.0007196835,0.001167248],"category_scores_gemma":[0.002209305,0.0002523395,0.0002888589,0.0002094982,0.001065646,0.0007462765,0.0006396658,0.0007844589,0.0001917022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000782816,"about_ca_system_score_gemma":0.000957897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004769818,"about_ca_topic_score_gemma":0.003271256,"domain_scores_codex":[0.9996243,0.0001237645,0.00001895748,0.00007563346,0.00009748618,0.00005989362],"domain_scores_gemma":[0.9989645,0.0004979289,0.0001470034,0.0001144726,0.0002028773,0.00007320842],"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":[0.00003545896,0.00004613835,0.0004000739,0.00001455312,0.00001586091,0.00002642142,0.00003499712,0.9685881,0.00128548,0.01348909,0.0003056129,0.01575824],"study_design_scores_gemma":[0.000008692055,0.00001580331,0.00004561288,0.000001527402,0.000002766916,0.000003812293,0.000003212005,0.9955106,0.0004333198,0.003550323,0.0004213524,0.000002941768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1017604,0.0002143543,0.887979,0.0003785872,0.00007056498,0.00009662186,0.00002932754,0.0005641708,0.008907031],"genre_scores_gemma":[0.9376878,0.0001374908,0.05815348,0.00009226484,0.00003268601,0.0001272659,0.00003649582,0.00003299928,0.003699666],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004769818,"threshold_uncertainty_score":0.009484112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005686180185488333,"score_gpt":0.1883398676459957,"score_spread":0.1826536874605074,"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."}}