{"id":"W2168356773","doi":"10.48550/arxiv.1207.1411","title":"Bayes' Bluff: Opponent Modelling in Poker","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":149,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Prior probability; Computer science; Adversary; Artificial intelligence; Dirichlet distribution; Fictitious play; Bluff; Probabilistic logic; Domain (mathematical analysis); Task (project management); Class (philosophy); Observability; Machine learning; Bayesian probability; Game theory; Mathematical economics; Mathematics; Applied mathematics","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":[0.0005052568,0.0003677339,0.0003805545,0.0004269448,0.00009938222,0.0001414596,0.00251866,0.0003844648,0.0000696698],"category_scores_gemma":[0.00002580271,0.0004376181,0.0002093119,0.0005962825,0.0001184848,0.0006884379,0.002745948,0.0008630676,0.0004813795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003694214,"about_ca_system_score_gemma":0.0001779341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007505069,"about_ca_topic_score_gemma":0.0001111134,"domain_scores_codex":[0.9974905,0.0001717455,0.0003571929,0.001182429,0.0001443363,0.0006538117],"domain_scores_gemma":[0.9978027,0.0001402719,0.0002344944,0.001463792,0.0001231636,0.000235518],"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":[0.00001095257,0.0001115781,0.002683467,0.000024639,0.00002202017,0.0001596184,0.0008223259,0.8072248,0.00002070808,0.1876923,0.00006116094,0.001166379],"study_design_scores_gemma":[0.00006976649,0.0000119485,0.0001896293,0.00009123042,0.00001899286,0.000002531488,0.0001002328,0.9152979,0.0005053583,0.08272331,0.0005354545,0.0004536277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2154227,0.0001552278,0.7799593,0.0001336349,0.0009234475,0.0002398835,0.000003638947,0.0001821816,0.002980049],"genre_scores_gemma":[0.9914989,0.0002195664,0.007108184,0.00009497366,0.0001248714,0.000001612946,0.000004858695,0.00002306199,0.0009239821],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7760762,"threshold_uncertainty_score":0.9998075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1747774249227732,"score_gpt":0.2159533918694924,"score_spread":0.04117596694671916,"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."}}