{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003018792,0.0001746341,0.0001738108,0.0002823741,0.0001848569,0.0006319874,0.0003479539,0.00008441258,0.000004374629],"category_scores_gemma":[0.00005433799,0.0001386972,0.00002938258,0.0005879015,0.00005231674,0.004649561,0.00005704898,0.0001709925,0.0000764943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001569534,"about_ca_system_score_gemma":0.00007887545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001326892,"about_ca_topic_score_gemma":0.00001371565,"domain_scores_codex":[0.9983499,0.00005812383,0.0005890245,0.000152782,0.0004684085,0.0003817708],"domain_scores_gemma":[0.9989812,0.00005424459,0.0003667512,0.0002614011,0.0002431949,0.00009317014],"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":[0.00003639139,0.00007355898,0.007611139,0.0001834856,0.00001004841,0.000001824332,0.03354396,0.9246681,0.0000664827,0.002225715,0.000533551,0.03104572],"study_design_scores_gemma":[0.0001117128,0.00004089285,0.001931101,0.0001474455,0.000004481368,0.00006809832,0.002690767,0.9916565,0.0006142217,0.000003290116,0.002546065,0.0001854426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5994927,0.0002946703,0.3965041,0.0002953394,0.001044571,0.0004798117,0.000004616962,0.0002919965,0.00159212],"genre_scores_gemma":[0.9985308,0.000002324427,0.001047895,0.0001463492,0.00008326397,0.00006677184,0.000004109037,0.000008438375,0.0001100311],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3990381,"threshold_uncertainty_score":0.6094269,"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."}}