{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006591058,0.0004185897,0.0003524404,0.0006227194,0.0001594264,0.000652567,0.002148338,0.0001436414,0.0005946205],"category_scores_gemma":[0.0007018613,0.0003177436,0.0001103518,0.0005091859,0.0002009554,0.001929725,0.0003339196,0.0003459617,0.0005244374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004053767,"about_ca_system_score_gemma":0.0001558524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003672186,"about_ca_topic_score_gemma":0.0009348285,"domain_scores_codex":[0.9958732,0.0003237248,0.001134394,0.001135614,0.00101406,0.0005190648],"domain_scores_gemma":[0.9978935,0.0002949068,0.0003161061,0.0007922951,0.000511234,0.0001919807],"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":[0.0001160792,0.001749164,0.001388727,0.000001743699,0.0000273239,0.00006315906,0.006002237,0.00025277,0.08234639,0.683867,0.00003358371,0.2241519],"study_design_scores_gemma":[0.0001722926,0.001671709,0.003156383,0.0003208531,0.000005443903,0.00001939022,0.009842928,0.04757351,0.8002218,0.1359771,0.0001505203,0.0008880399],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3277345,0.00001561593,0.6611191,0.003283808,0.002510916,0.00069861,0.00001219779,0.0003209744,0.004304276],"genre_scores_gemma":[0.9950317,0.00001598406,0.004089125,0.0002704916,0.0002516187,0.0001194317,0.000006527977,0.00002591889,0.0001892155],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7178754,"threshold_uncertainty_score":0.9999275,"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."}}