{"id":"W7100116940","doi":"","title":"University of Alberta Move Groups as a General Enhancement for Monte Carlo Tree Search","year":2016,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Monte Carlo tree search; Tree (set theory); Permission; Game tree; Monte Carlo method; Field (mathematics); Group (periodic table)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001518797,0.00008246348,0.0001195889,0.00005198404,0.0000636615,0.00001855616,0.0007619457,0.00003925059,0.0001681306],"category_scores_gemma":[0.00003778575,0.0000609555,0.00008161057,0.0001164361,0.00008728159,0.0002913998,0.000257108,0.00002731096,0.000128851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006400931,"about_ca_system_score_gemma":0.00007337972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00410525,"about_ca_topic_score_gemma":0.002292455,"domain_scores_codex":[0.9990959,0.00003060869,0.000136228,0.0002914381,0.0001983882,0.0002474074],"domain_scores_gemma":[0.9991068,0.0002266295,0.00004492786,0.0004025962,0.0001439057,0.00007519165],"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":[0.0001147039,0.000178567,0.0009285779,0.00001654909,0.00007101959,0.000008284359,0.005729999,0.0001671181,0.09655011,0.4535535,0.004166514,0.438515],"study_design_scores_gemma":[0.0003603848,0.0005157659,0.0004579504,0.00003313949,0.000009524143,0.000002573093,0.0005206855,0.0884226,0.8904727,0.009702004,0.009221961,0.0002807459],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2963179,0.00001141691,0.6957805,0.001649357,0.0001077146,0.0001816057,0.000001677026,0.00002267389,0.005927139],"genre_scores_gemma":[0.9207175,0.00001979812,0.03626948,0.00008152764,0.00003464566,0.000002424991,1.975209e-7,0.000004661279,0.04286972],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7939225,"threshold_uncertainty_score":0.6205937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02940079898873384,"score_gpt":0.2699716982427975,"score_spread":0.2405708992540637,"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."}}