{"id":"W2161232659","doi":"10.1109/cig.2008.5035658","title":"Learning and knowledge generation in General Games","year":2008,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Variety (cybernetics); Reinforcement learning; Artificial intelligence; Heuristic; Representation (politics); Tree (set theory); Machine learning; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002594621,0.0006897427,0.0008653768,0.0007528716,0.0006847947,0.00175204,0.001698981,0.0013882,0.002387503],"category_scores_gemma":[0.01590875,0.0004685585,0.000900122,0.0006287813,0.003180273,0.003430478,0.002578354,0.001599786,0.0002667766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00132036,"about_ca_system_score_gemma":0.001174902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003736757,"about_ca_topic_score_gemma":0.002869843,"domain_scores_codex":[0.9977852,0.001263005,0.0001079492,0.0003564394,0.0003276291,0.0001597863],"domain_scores_gemma":[0.9927917,0.005362488,0.0004397051,0.0007422076,0.0003723506,0.0002916188],"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":[0.0001795786,0.0003086331,0.001596911,0.0002999206,0.0001200903,0.0002723043,0.001179785,0.5855092,0.00262027,0.3324959,0.001265737,0.07415166],"study_design_scores_gemma":[0.00006594959,0.00009092732,0.0003431574,0.00003206366,0.0000259331,0.0000736427,0.0001414606,0.6991036,0.001377536,0.2962233,0.002505522,0.00001698575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1032222,0.0003433625,0.8825979,0.00105064,0.00003585985,0.0002637877,0.00006884582,0.0002945653,0.01212279],"genre_scores_gemma":[0.7312282,0.000392431,0.2630962,0.0002014758,0.0000299866,0.0003528619,0.0001976705,0.00005246365,0.004448668],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003736757,"threshold_uncertainty_score":0.01372182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05657304517002479,"score_gpt":0.3060167380439587,"score_spread":0.2494436928739339,"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."}}