{"id":"W2121328774","doi":"","title":"Efficient Monte Carlo Counterfactual Regret Minimization in Games with Many Player Actions","year":2012,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Counterfactual thinking; Computer science; Monte Carlo tree search; Regret; Monte Carlo method; Mathematical optimization; Game tree; Thompson sampling; Limit (mathematics); Minification; Tree (set theory); Algorithm; Mathematics; Game theory; Repeated game; Machine learning; Mathematical economics; Statistics","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":[0.008456931,0.001457021,0.002204529,0.001027703,0.000946396,0.00186977,0.002465799,0.0021838,0.002490648],"category_scores_gemma":[0.0333282,0.001109198,0.001022536,0.001089571,0.002591321,0.002970869,0.001961415,0.002872972,0.0003787523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002642218,"about_ca_system_score_gemma":0.003003319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006451937,"about_ca_topic_score_gemma":0.007292533,"domain_scores_codex":[0.9954222,0.002969978,0.0001322846,0.0004912863,0.0006526407,0.0003315508],"domain_scores_gemma":[0.9738057,0.02307404,0.0009784092,0.0009623526,0.00070235,0.0004771948],"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":[0.0001658459,0.00008001228,0.0007058127,0.00007543502,0.0000640541,0.00005830212,0.00007754383,0.9141735,0.0002959527,0.06478119,0.0009325572,0.01858974],"study_design_scores_gemma":[0.00001815304,0.0000178713,0.00006158965,0.000009100055,0.000006453213,0.00001113171,0.000005698244,0.9739135,0.0001650154,0.02559965,0.0001872296,0.00000458405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02862753,0.000512472,0.9652584,0.0006507576,0.00003743128,0.0001342203,0.00005727699,0.0003344535,0.00438747],"genre_scores_gemma":[0.6520774,0.0004436033,0.3421046,0.0004867736,0.00009078512,0.000510641,0.0002242791,0.0002054551,0.003856385],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008456931,"threshold_uncertainty_score":0.04472506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03613825230177032,"score_gpt":0.2768897801202274,"score_spread":0.240751527818457,"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."}}