{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00800558,0.00135739,0.002161027,0.0009697944,0.0007053511,0.001695611,0.002749018,0.001904571,0.003104898],"category_scores_gemma":[0.03598407,0.0007861231,0.001090745,0.001165968,0.002810283,0.002612216,0.002290341,0.00257411,0.0003653717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002438308,"about_ca_system_score_gemma":0.002785396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004072148,"about_ca_topic_score_gemma":0.004077902,"domain_scores_codex":[0.9944409,0.003866573,0.000172555,0.0004498097,0.0006785222,0.0003916134],"domain_scores_gemma":[0.9605395,0.03402811,0.001822158,0.001771256,0.0009203101,0.0009188583],"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":[0.0003072198,0.0001421458,0.001074926,0.00009937818,0.0000838039,0.0000778897,0.00008300318,0.9181818,0.0002901824,0.06568128,0.000955882,0.01302235],"study_design_scores_gemma":[0.00002111495,0.00002498031,0.0000730475,0.000008341447,0.000007525197,0.000008764233,0.000004421621,0.9758399,0.0001061621,0.02373242,0.0001690756,0.00000430994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0646653,0.0007324622,0.9250493,0.001128042,0.00008978359,0.0002314206,0.0001558778,0.0004542067,0.007493571],"genre_scores_gemma":[0.8600533,0.0004017393,0.1346481,0.0004145306,0.0001083219,0.0005578134,0.0002773493,0.0001674531,0.003371339],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00800558,"threshold_uncertainty_score":0.04233801,"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."}}