{"id":"W1540112079","doi":"10.5555/1402821.1402866","title":"Using adaptive consultation of experts to improve convergence rates in multiagent learning","year":2008,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Regret; Computer science; Outcome (game theory); Convergence (economics); Advice (programming); Multi-agent system; Set (abstract data type); Class (philosophy); Nash equilibrium; Process (computing); Frame (networking); Order (exchange); Artificial intelligence; Machine learning; Mathematical optimization; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001380614,0.0003250065,0.000675172,0.0006197505,0.0003308475,0.00008605754,0.0004379018,0.0001278934,0.00007067435],"category_scores_gemma":[0.002300886,0.000265593,0.00008983129,0.0009860127,0.0002778324,0.0006866425,0.0003172439,0.0002277131,0.00006998549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001936157,"about_ca_system_score_gemma":0.0001270729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002318706,"about_ca_topic_score_gemma":0.00008669018,"domain_scores_codex":[0.9950941,0.0006916448,0.001172699,0.0009642953,0.001499667,0.0005775738],"domain_scores_gemma":[0.9968109,0.0009305387,0.0005401592,0.000376847,0.001015928,0.0003256742],"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":[0.001990961,0.001611979,0.2587844,0.0001242976,0.0004772068,0.0009695367,0.0912426,0.5048854,0.09240387,0.0002212909,0.001034741,0.04625373],"study_design_scores_gemma":[0.003003147,0.0003891104,0.05526975,0.0002247289,0.00001067557,0.0000242491,0.02737834,0.9081517,0.004278557,0.00002672428,0.0008329775,0.0004100435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8197591,0.0007808242,0.1771438,0.00001388806,0.0006300095,0.001531903,0.00004987558,0.00002210159,0.00006856683],"genre_scores_gemma":[0.9917575,0.0002357684,0.006328448,0.00003332399,0.0000522177,0.00009751283,0.000004917284,0.00003124081,0.001459106],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4032663,"threshold_uncertainty_score":0.9999796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.367641049994208,"score_gpt":0.4650858587974098,"score_spread":0.09744480880320183,"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."}}