{"id":"W2574227367","doi":"","title":"Monte Carlo tree search in continuous action spaces with execution uncertainty","year":2016,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Monte Carlo tree search; Computer science; Set (abstract data type); Fidelity; Machine learning; Tree (set theory); Action (physics); Artificial intelligence; Monte Carlo method; Kernel (algebra); Domain (mathematical analysis); Mathematics","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":[0.002047504,0.0006157301,0.001212315,0.0006734459,0.0004630728,0.0007626329,0.001062305,0.001342961,0.001768956],"category_scores_gemma":[0.008587288,0.0005909461,0.0006600482,0.0007883928,0.001436723,0.001182651,0.0009241236,0.001259584,0.0002150587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001271818,"about_ca_system_score_gemma":0.00188491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01426092,"about_ca_topic_score_gemma":0.01163916,"domain_scores_codex":[0.999284,0.0003506508,0.00003504619,0.0001063561,0.0001438719,0.0000800983],"domain_scores_gemma":[0.9935209,0.005526104,0.000327226,0.0001738406,0.0002714572,0.0001804704],"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":[0.00003303061,0.00001123025,0.0003225083,0.00001576001,0.00001027455,0.00002048849,0.00001592606,0.9880079,0.0001163809,0.006791281,0.0001355556,0.004519665],"study_design_scores_gemma":[0.000004039902,0.000004602573,0.00003460298,0.000001721847,0.000001261776,0.000002422461,0.000001679347,0.9972844,0.00003068319,0.00258071,0.00005271592,0.000001181228],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06569245,0.0005481931,0.9305221,0.0003357963,0.00003200927,0.00005248046,0.00007413647,0.0003441707,0.002398659],"genre_scores_gemma":[0.8088353,0.0002670766,0.1883175,0.0001387605,0.00003413007,0.000172885,0.000194037,0.0001072763,0.001932993],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01426092,"threshold_uncertainty_score":0.02835584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1274409482000282,"score_gpt":0.3388143823562566,"score_spread":0.2113734341562284,"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."}}