{"id":"W3185452770","doi":"10.20944/preprints202107.0622.v1","title":"An Extensible and Modular Design and Implementation of Monte Carlo Tree Search for the JVM","year":2021,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Modular design; Implementation; Heuristics; Programming language; Key (lock); Monte Carlo tree search; Theoretical computer science; Software engineering; Monte Carlo method; Operating system","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":[0.001590974,0.000206966,0.0002925952,0.00009645425,0.0001550648,0.0001505871,0.0009665643,0.0001261927,0.00002537064],"category_scores_gemma":[0.00008363217,0.0001794943,0.00008035074,0.0001265068,0.0001590763,0.0003068013,0.001949715,0.0002715169,0.000003798049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003947979,"about_ca_system_score_gemma":0.0002042302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001570815,"about_ca_topic_score_gemma":0.0001769072,"domain_scores_codex":[0.9978316,0.0002558344,0.0004201354,0.0009113752,0.0003079851,0.0002730148],"domain_scores_gemma":[0.9975309,0.0003892625,0.0001818343,0.001415671,0.0003891689,0.00009314388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001034228,0.0002318728,0.3038674,0.0005629683,0.0004625278,0.00002278465,0.05445785,0.1320103,0.1671196,0.008478723,0.0000341346,0.3326485],"study_design_scores_gemma":[0.0001055799,0.00006550793,0.1440161,0.0000546433,0.0000420312,0.000007141756,0.002373231,0.3542229,0.495204,0.003651671,0.00004051199,0.0002166727],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5994041,0.000371813,0.398813,0.0003640042,0.0001391847,0.0008512434,0.000004689222,0.00003465409,0.00001729933],"genre_scores_gemma":[0.9691657,0.0003739158,0.03009957,0.00004671946,0.0000501276,0.0002026633,0.000003070716,0.00001761825,0.00004064458],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3697616,"threshold_uncertainty_score":0.7319564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2388369012259718,"score_gpt":0.4222757360897955,"score_spread":0.1834388348638237,"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."}}