{"id":"W2102256448","doi":"10.1109/tciaig.2010.2067212","title":"Monte Carlo Tree Search in Hex","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Computational Intelligence and AI in Games","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":143,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Monte Carlo tree search; Monte Carlo method; Game tree; Computer science; Tree (set theory); Olympiad; Theoretical computer science; Algorithm; Artificial intelligence; Game theory; Mathematics; Mathematical economics; Combinatorics; Statistics; Sequential game","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":[],"consensus_categories":[],"category_scores_codex":[0.0003989941,0.0002199031,0.0002275694,0.000624639,0.0001249583,0.0001955512,0.0006215303,0.0001318436,0.00005365762],"category_scores_gemma":[0.00001922295,0.0002228083,0.00007602745,0.0008395683,0.0002471022,0.0006672484,0.00001014908,0.0008750228,0.00009688057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000527136,"about_ca_system_score_gemma":0.000134677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005261312,"about_ca_topic_score_gemma":0.002583538,"domain_scores_codex":[0.9980324,0.00008689403,0.0005124341,0.0005807362,0.0004064595,0.0003810936],"domain_scores_gemma":[0.9987434,0.0006367431,0.00004787005,0.0003120099,0.0001393617,0.0001206008],"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":[0.00001829125,0.0002134908,0.0004251898,0.000008254407,0.000007421986,0.00002589323,0.001856137,0.6388329,0.0003911918,0.006161002,0.00002081992,0.3520395],"study_design_scores_gemma":[0.00008761357,0.0001284416,0.003476185,0.00006085937,0.000002701852,0.00003185785,0.0003924294,0.9486735,0.02398839,0.0227119,0.0001513271,0.000294761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2371921,0.00004686624,0.7598346,0.001873305,0.0005640684,0.0001925072,0.000004252677,0.00006922523,0.0002230843],"genre_scores_gemma":[0.9836237,0.0000713429,0.0155313,0.000514993,0.00003745809,0.00004476185,5.230171e-7,0.0000130273,0.0001629097],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7464315,"threshold_uncertainty_score":0.908586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03421570580501265,"score_gpt":0.3124778418136939,"score_spread":0.2782621360086812,"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."}}