{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003721468,0.0007646056,0.001611725,0.0007198714,0.0005419235,0.001087734,0.001358845,0.001327646,0.001307886],"category_scores_gemma":[0.01731602,0.0006035497,0.0007426338,0.001027638,0.001364945,0.001661071,0.001421538,0.001692494,0.0001985742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001396343,"about_ca_system_score_gemma":0.002166156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009961767,"about_ca_topic_score_gemma":0.007756145,"domain_scores_codex":[0.9979438,0.001091229,0.0001001899,0.0002817229,0.0003689556,0.0002140957],"domain_scores_gemma":[0.9878255,0.01012833,0.0005586061,0.0006876001,0.0005242822,0.0002757233],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006993039,0.00001703355,0.0005538741,0.00002152712,0.00001910915,0.00003483693,0.00001992902,0.984797,0.0002056649,0.007249217,0.000339472,0.006672397],"study_design_scores_gemma":[0.000006534075,0.000007940113,0.00004864438,0.000002636362,0.000002726565,0.000006355908,0.000001992611,0.9955095,0.00008934145,0.004234749,0.00008800249,0.000001645736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1052787,0.0007309485,0.8888373,0.0006789683,0.00005692551,0.00008938404,0.0001843634,0.000804768,0.003338678],"genre_scores_gemma":[0.8395087,0.0002004801,0.1582972,0.0002185033,0.00004466802,0.0001300655,0.0002800132,0.0001335562,0.001186792],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009961767,"threshold_uncertainty_score":0.01980752,"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."}}