{"id":"W4393160714","doi":"10.1609/aaai.v38i18.29994","title":"Monte Carlo Tree Search in the Presence of Transition Uncertainty","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Modeling, Simulation, and Optimization","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); University of Alberta","funders":"","keywords":"Monte Carlo method; Statistical physics; Monte Carlo tree search; Tree (set theory); Transition (genetics); Computer science; Mathematics; Physics; Statistics; Chemistry; Combinatorics","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":[0.001096539,0.0001453602,0.0002013585,0.000138937,0.00008250603,0.00009892155,0.0005972462,0.00008446939,0.00005390496],"category_scores_gemma":[0.000451414,0.00009124884,0.0001040851,0.0006279906,0.0001836265,0.000215755,0.0000390589,0.0002708744,0.000005910041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003606605,"about_ca_system_score_gemma":0.000087797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002156518,"about_ca_topic_score_gemma":0.000137981,"domain_scores_codex":[0.9983844,0.00004344005,0.000568345,0.0002665505,0.000542448,0.0001948705],"domain_scores_gemma":[0.9987758,0.0003860109,0.000134088,0.0001821116,0.0004960839,0.00002589867],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001556132,0.0001960149,0.00007808811,0.0004568889,0.0000189312,5.923721e-7,0.03950406,0.08195811,0.006266482,0.8561252,0.0001464399,0.01509359],"study_design_scores_gemma":[0.00002005809,0.00007485723,0.00003456236,0.0004865214,0.00002201814,0.000001209162,0.003232296,0.7704886,0.02786126,0.1976845,0.00001018531,0.00008394545],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9742657,0.00006430144,0.01721067,0.002491499,0.0001858743,0.0008288721,0.00001467761,0.00005067176,0.004887753],"genre_scores_gemma":[0.9988859,0.00007185298,0.000810465,0.00002997277,0.00005282254,0.00002255952,7.136454e-7,0.00001288466,0.0001127817],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6885304,"threshold_uncertainty_score":0.372102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.157741851052406,"score_gpt":0.3592083977818272,"score_spread":0.2014665467294211,"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."}}