{"id":"W1646839507","doi":"10.1609/aaai.v24i1.7562","title":"Understanding the Success of Perfect Information Monte Carlo Sampling in Game Tree Search","year":2010,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Monte Carlo tree search; Perfect information; Computer science; Set (abstract data type); A priori and a posteriori; Monte Carlo method; Variety (cybernetics); Search algorithm; Imperfect; Algorithm; Mathematical economics; Artificial intelligence; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.001751166,0.0002604859,0.0003337966,0.0003453076,0.0002273764,0.0004191328,0.003488811,0.0001503561,0.00004295231],"category_scores_gemma":[0.001025029,0.0001756133,0.0001538444,0.001370681,0.0007377394,0.001455667,0.0005802144,0.0008940817,0.0000379205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000103905,"about_ca_system_score_gemma":0.0001936517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004167835,"about_ca_topic_score_gemma":0.0006078813,"domain_scores_codex":[0.9972038,0.00004205525,0.001023291,0.0003843709,0.000853996,0.0004924829],"domain_scores_gemma":[0.9976285,0.0004591841,0.0005379809,0.0005316701,0.000760904,0.00008183521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006963784,0.0000880505,0.003800087,0.00005440896,0.00001258302,2.776243e-7,0.01238327,0.001721237,0.03396107,0.9001586,0.00002215555,0.04772862],"study_design_scores_gemma":[0.00003084061,0.0001688538,0.001765795,0.000263438,0.00001038754,0.000006123158,0.006250085,0.3081273,0.5423823,0.1406736,0.00004334671,0.0002780166],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8948166,0.00001681512,0.09356567,0.003371078,0.0007892637,0.0007226591,0.000005448605,0.00007053247,0.006641938],"genre_scores_gemma":[0.9985576,0.00004193201,0.001182187,0.0000972261,0.00005497609,0.00002584639,2.380174e-7,0.00001120125,0.0000288219],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7594851,"threshold_uncertainty_score":0.7161303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1901535286000289,"score_gpt":0.3337788850530906,"score_spread":0.1436253564530617,"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."}}