{"id":"W7124220798","doi":"10.65109/qfxi4541","title":"Online Monte Carlo Counterfactual Regret Minimization for Search in Imperfect Information Games","year":2015,"lang":"","type":"article","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Counterfactual thinking; Monte Carlo tree search; Regret; Perfect information; Monte Carlo method; Outcome (game theory); Set (abstract data type); Search algorithm; Convergence (economics)","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":[0.001562215,0.0003879162,0.0004480812,0.0005781228,0.0001267437,0.0007976882,0.001154902,0.00029339,0.00006937507],"category_scores_gemma":[0.00120523,0.0003722505,0.000157608,0.0009736202,0.0002093982,0.005176843,0.0004108556,0.0003354615,0.000213689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005223551,"about_ca_system_score_gemma":0.001004208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001779191,"about_ca_topic_score_gemma":0.001537174,"domain_scores_codex":[0.9961758,0.000214171,0.001341373,0.0005597483,0.0008840886,0.0008248602],"domain_scores_gemma":[0.9967626,0.0004403146,0.0002752299,0.0007401695,0.001464842,0.0003167943],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005984732,0.0006231791,0.005736744,0.0001685941,0.00006791821,0.000008679254,0.09717531,0.2888613,0.00005628281,0.004949567,0.01580297,0.585951],"study_design_scores_gemma":[0.0005593721,0.0008625296,0.0005354714,0.0001041826,0.00001395809,0.00001054287,0.008022631,0.9768164,0.00515684,0.0004826671,0.007014523,0.0004209409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3388202,0.0003224456,0.6544062,0.002342303,0.00155505,0.00148208,0.00009185945,0.0001256717,0.000854161],"genre_scores_gemma":[0.9785088,0.0001214149,0.01949225,0.0006293007,0.0002292688,0.0000524172,0.00004414284,0.00002154434,0.0009008988],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6879551,"threshold_uncertainty_score":0.9998729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08296548613523219,"score_gpt":0.3356971728778488,"score_spread":0.2527316867426166,"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."}}