{"id":"W7124160341","doi":"10.65109/sfps9972","title":"Using counterfactual regret minimization to create competitive multiplayer poker agents","year":2010,"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":"Perfect information; Counterfactual thinking; Regret; Nash equilibrium; Event (particle physics); Complete information; Extensive-form game; Imperfect","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","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005263477,0.0005796394,0.0004846905,0.0003497449,0.0005322279,0.001049255,0.00159145,0.0003598479,0.004959637],"category_scores_gemma":[0.0005757377,0.0005732141,0.0002072495,0.0008585685,0.000375554,0.001381568,0.0009684319,0.0006265548,0.002864794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001580656,"about_ca_system_score_gemma":0.0002913634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007929762,"about_ca_topic_score_gemma":0.0008677529,"domain_scores_codex":[0.9956215,0.0001846555,0.0009847624,0.001337807,0.000887228,0.0009840325],"domain_scores_gemma":[0.996581,0.0003438083,0.0003174287,0.001233099,0.0009451113,0.0005795241],"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":[0.0009490608,0.002937035,0.04305571,0.0001483239,0.0008770595,0.0004791102,0.1379246,0.1205505,0.2099841,0.2190405,0.01808415,0.2459699],"study_design_scores_gemma":[0.0001762341,0.0001872699,0.001827357,0.0001154061,0.0000412741,0.00003605353,0.00101063,0.8768848,0.1075336,0.0002861139,0.01115602,0.000745319],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3817541,0.0000151315,0.6017688,0.0008453612,0.004844194,0.0006955542,0.00002781308,0.0001341125,0.009914923],"genre_scores_gemma":[0.8699595,0.000011234,0.1244029,0.0018938,0.0004149164,0.00001069633,0.000004931649,0.0000466239,0.003255334],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7563342,"threshold_uncertainty_score":0.9999878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09120569337096288,"score_gpt":0.3504002763899025,"score_spread":0.2591945830189396,"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."}}