{"id":"W2966266863","doi":"","title":"Integrating Factorization Ranked Features in MCTS: An Experimental Study.","year":2016,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Ranking (information retrieval); Factorization; Computer science; Factor (programming language); Feature (linguistics); Tree (set theory); Artificial intelligence; Monte Carlo tree search; Machine learning; Monte Carlo method; Open source; State (computer science); Data mining; Algorithm; Statistics; Software; Mathematics; Programming language","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":[0.002217564,0.001366653,0.00120953,0.001444842,0.0006162578,0.0008066907,0.001764106,0.001490773,0.005966485],"category_scores_gemma":[0.01120677,0.0003011057,0.0008058049,0.001386411,0.0003425365,0.001824696,0.0009382658,0.001303907,0.001690505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001207083,"about_ca_system_score_gemma":0.001517155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03140693,"about_ca_topic_score_gemma":0.03977955,"domain_scores_codex":[0.9987472,0.0004454178,0.00006071304,0.0003000821,0.0003054525,0.0001410637],"domain_scores_gemma":[0.9965786,0.002091084,0.0001484259,0.000489944,0.0004466397,0.0002452388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004848806,0.003945291,0.02191384,0.0007039261,0.0005605248,0.0004436499,0.0002129301,0.240915,0.008125221,0.003560215,0.03950297,0.6752676],"study_design_scores_gemma":[0.0001382603,0.0004105512,0.002190151,0.00001657317,0.00006909248,0.00006705402,0.00004886507,0.9914709,0.002163481,0.001373025,0.002036338,0.0000157382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7166055,0.004883752,0.2360043,0.001073837,0.0009826726,0.001397174,0.006661596,0.01726295,0.01512821],"genre_scores_gemma":[0.8215652,0.0002965501,0.1661257,0.0001839761,0.00006213177,0.0002410455,0.006717782,0.0002623648,0.004545378],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03140693,"threshold_uncertainty_score":0.06244826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1155765743751976,"score_gpt":0.3635510990341819,"score_spread":0.2479745246589843,"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."}}