{"id":"W2950348125","doi":"","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":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Prior probability; Computer science; Adversary; Fictitious play; Artificial intelligence; Dirichlet distribution; Probabilistic logic; Task (project management); Domain (mathematical analysis); Class (philosophy); Observability; Bayesian game; Bluff; Stochastic game; Machine learning; Bayesian probability; Game theory; Mathematical economics; Mathematics; Sequential game; Computer security","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":[0.006155275,0.0009669481,0.00132785,0.0008311048,0.001143875,0.00246347,0.002374522,0.003068252,0.006529428],"category_scores_gemma":[0.02312895,0.0009408157,0.00108259,0.0007112446,0.004438133,0.004734991,0.002839337,0.00468517,0.0009717094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001953593,"about_ca_system_score_gemma":0.001425671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005060778,"about_ca_topic_score_gemma":0.004970368,"domain_scores_codex":[0.9970803,0.00168247,0.00007801743,0.0004073853,0.0005216706,0.0002302546],"domain_scores_gemma":[0.990912,0.00782791,0.0003760727,0.0004297512,0.0002418501,0.0002123873],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008549137,0.00002750036,0.0008712623,0.00007105771,0.00003887022,0.0001721061,0.0004826267,0.1889708,0.0003941603,0.7923359,0.001638444,0.01491181],"study_design_scores_gemma":[0.00001429599,0.00001153821,0.0001132167,0.00001957122,0.00000752576,0.0000440608,0.00003602028,0.4691331,0.0001575965,0.5291433,0.001305429,0.00001439662],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01288427,0.0003063316,0.9739645,0.001821977,0.0000651122,0.0000409893,0.00007659394,0.0001323418,0.01070787],"genre_scores_gemma":[0.7836615,0.0007294652,0.1942613,0.0009979212,0.0002457337,0.0002689771,0.000182684,0.0002059562,0.0194465],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006529428,"threshold_uncertainty_score":0.03255266,"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."}}