{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007291627,0.000401137,0.0003812599,0.0006601332,0.0001400185,0.0005470954,0.001578386,0.0001650034,0.0003241542],"category_scores_gemma":[0.0003630774,0.0002860048,0.0001220434,0.0006064886,0.0004207474,0.001231009,0.0002515546,0.0003949125,0.0008356245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005361977,"about_ca_system_score_gemma":0.0002856308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001222838,"about_ca_topic_score_gemma":0.003800967,"domain_scores_codex":[0.9960181,0.0002190041,0.0008817129,0.00106677,0.001149573,0.0006648289],"domain_scores_gemma":[0.9975919,0.0003819852,0.0002836057,0.0007176228,0.0008311545,0.0001937731],"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":[0.0002853809,0.0002582356,0.0007406359,0.00000517758,0.00003274597,0.00009253422,0.0010631,0.006276251,0.02138546,0.3994014,0.00006966415,0.5703894],"study_design_scores_gemma":[0.0002530162,0.001343192,0.003553318,0.001142768,0.00001384795,0.00008605075,0.003920529,0.3975709,0.4916044,0.09766763,0.001498162,0.00134609],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3718368,0.0000178476,0.601288,0.01789185,0.00146352,0.0005480458,0.00001900984,0.0002978016,0.006637093],"genre_scores_gemma":[0.9956497,0.0001233486,0.002930027,0.0002179043,0.0002063122,0.00008645521,0.00000243487,0.00002348251,0.0007603215],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6238129,"threshold_uncertainty_score":0.9999592,"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."}}