{"id":"W4402007577","doi":"10.1007/978-3-031-70893-0_2","title":"Efficiently Training Neural Networks for Imperfect Information Games by Sampling Information Sets","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Perfect information; Artificial neural network; Training (meteorology); Imperfect; Artificial intelligence; Sampling (signal processing); Machine learning; Computer vision; Mathematics; Mathematical 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":[0.001877813,0.001569976,0.002008638,0.0008177201,0.0005680486,0.001381012,0.002348042,0.002585382,0.003974993],"category_scores_gemma":[0.01235875,0.001463617,0.0008453827,0.0007897471,0.001381577,0.003178477,0.002318304,0.003355647,0.0005597434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002041171,"about_ca_system_score_gemma":0.001606191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00737338,"about_ca_topic_score_gemma":0.01262331,"domain_scores_codex":[0.9992535,0.0002721002,0.00004775231,0.0001683824,0.0001237837,0.0001345553],"domain_scores_gemma":[0.9918535,0.007137494,0.0002766937,0.0003019942,0.0002813709,0.0001488822],"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":[0.0001302464,0.00007866487,0.0004315979,0.00006837789,0.00004211896,0.0000307997,0.00003686101,0.9536524,0.0004665166,0.01226394,0.001137069,0.03166148],"study_design_scores_gemma":[0.00000781569,0.00001003344,0.00001947489,0.00000455518,0.000002829749,0.000002859841,0.000003030569,0.9926536,0.0000923474,0.007155196,0.00004669471,0.000001627206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1017349,0.0008994798,0.8868212,0.001114046,0.0001437299,0.0001848318,0.0002512387,0.00105371,0.007796864],"genre_scores_gemma":[0.8166174,0.0004012828,0.1731752,0.0003700914,0.000140649,0.0004416859,0.0004868786,0.0002082094,0.008158436],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00737338,"threshold_uncertainty_score":0.01480985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02881157661154738,"score_gpt":0.2810092525429389,"score_spread":0.2521976759313915,"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."}}