{"id":"W2479680810","doi":"10.4018/978-1-60566-226-8.ch011","title":"Learning Agents for Collaborative Driving","year":2009,"lang":"en","type":"book-chapter","venue":"Advances in mechatronics and mechanical engineering (AMME) book series","topic":"Traffic control and management","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Reinforcement learning; Context (archaeology); Domain (mathematical analysis); Computer science; Architecture; Control (management); Intelligent transportation system; Field (mathematics); Intelligent agent; Collaborative learning; Human–computer interaction; Agent architecture; Knowledge management; Systems engineering; Engineering; Artificial intelligence; 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":[0.0003267353,0.0005888484,0.0003302645,0.0002473166,0.0005243004,0.001281934,0.0007779544,0.001074958,0.009645388],"category_scores_gemma":[0.001066579,0.0002409154,0.0003791804,0.0002952742,0.0008300977,0.001743873,0.00130084,0.001622391,0.002708902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007434368,"about_ca_system_score_gemma":0.0005797631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009854711,"about_ca_topic_score_gemma":0.001165698,"domain_scores_codex":[0.9997841,0.00006154416,0.00001099255,0.00004746793,0.00007785931,0.00001813235],"domain_scores_gemma":[0.9997491,0.0001387903,0.00001630783,0.00003463697,0.00004037059,0.0000206882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000189469,0.00005613433,0.0001223918,0.0001905095,0.00002161931,0.00008597333,0.0004834555,0.03262149,0.001482888,0.7717125,0.01652587,0.1766782],"study_design_scores_gemma":[0.00002213137,0.00005241756,0.0001501233,0.0001428547,0.000015323,0.0001914198,0.0001235997,0.09758203,0.001682536,0.4500339,0.4499801,0.00002361297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006531143,0.0137273,0.733538,0.0027296,0.0007183843,0.0001198787,0.00008239612,0.0009419894,0.2416114],"genre_scores_gemma":[0.2862053,0.0181814,0.438588,0.001203581,0.0007937946,0.0005799863,0.0004383604,0.0004060507,0.2536036],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009645388,"threshold_uncertainty_score":0.03226709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004388899632410895,"score_gpt":0.1964455172060608,"score_spread":0.1920566175736499,"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."}}