{"id":"W2124520701","doi":"10.3233/mgs-2010-0153","title":"Task allocation learning in a multiagent environment: Application to the RoboCupRescue simulation","year":2010,"lang":"en","type":"article","venue":"Multiagent and Grid Systems","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Reinforcement learning; Task (project management); Perception; Human–computer interaction; Artificial intelligence; Multi-agent system; Distributed computing; Machine learning; Systems 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007502218,0.000152943,0.0001357991,0.0001137771,0.0002170673,0.0002358963,0.0004249322,0.0000818091,0.000003125238],"category_scores_gemma":[0.00008533685,0.000120251,0.0000302638,0.0001952158,0.00002283373,0.0002497599,0.0001975663,0.0003045447,0.0001571047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005443149,"about_ca_system_score_gemma":0.00001306609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002780591,"about_ca_topic_score_gemma":0.00005085073,"domain_scores_codex":[0.9985427,0.0001343092,0.0003542511,0.0003948721,0.0003307821,0.0002430587],"domain_scores_gemma":[0.9990675,0.0001498242,0.0001466182,0.0005111206,0.00003404523,0.00009082697],"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":[0.000002850494,0.00001722756,0.005304854,0.00001509456,0.000005908287,8.265248e-7,0.00189939,0.9789155,0.006431737,0.0005901851,0.00005103553,0.006765455],"study_design_scores_gemma":[0.00025472,0.00003254363,0.01668756,0.00001588011,0.000004257877,0.000002193693,0.00008468455,0.9326715,0.0001864977,0.00000270784,0.0499195,0.0001379808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04455509,0.00008102547,0.9528694,0.0006091131,0.000648329,0.001067165,6.396584e-7,0.00008748241,0.00008173393],"genre_scores_gemma":[0.99655,0.00001875915,0.002515756,0.00007031854,0.0002254072,0.0001744015,0.00001627259,0.0000123564,0.0004167877],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9519948,"threshold_uncertainty_score":0.4903692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01073146175779477,"score_gpt":0.2342188492319298,"score_spread":0.2234873874741351,"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."}}