{"id":"W2165622730","doi":"10.1287/opre.1080.0685","title":"Percentile Optimization for Markov Decision Processes with Parameter Uncertainty","year":2009,"lang":"en","type":"article","venue":"Operations Research","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":228,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; HEC Montréal","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Ambiguity; Computer science; Markov decision process; Mathematical optimization; Parametric statistics; Set (abstract data type); Percentile; Markov chain; Markov process; Process (computing); Decision process; Operations research; Machine learning; Mathematics; Management science; Statistics; Economics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.006993933,0.001576946,0.002485355,0.001345886,0.0008765465,0.002592529,0.001166665,0.001691012,0.003782567],"category_scores_gemma":[0.02178892,0.0008654148,0.001123594,0.001547827,0.00209013,0.003051362,0.002225716,0.002370783,0.0003751264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002589782,"about_ca_system_score_gemma":0.002039732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003353383,"about_ca_topic_score_gemma":0.002255916,"domain_scores_codex":[0.9964985,0.002053294,0.0001532632,0.0003292563,0.0005205522,0.0004452219],"domain_scores_gemma":[0.9868022,0.01115825,0.0008317214,0.0003323768,0.0004738014,0.0004016727],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007485536,0.00002417625,0.0004199674,0.00006666077,0.00003085938,0.00005775846,0.00005546414,0.8864178,0.000189374,0.1027275,0.000651727,0.00928375],"study_design_scores_gemma":[0.00000878478,0.00002115303,0.00009795922,0.00001482461,0.000005395881,0.00001025886,0.0000141948,0.9088791,0.0001075662,0.09044347,0.0003872211,0.000009940942],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02780702,0.001071051,0.9662752,0.0005303238,0.00004356119,0.00006296823,0.0001440092,0.0001898014,0.003875999],"genre_scores_gemma":[0.8519108,0.00208267,0.13873,0.0001873511,0.0001363558,0.0004074643,0.0004393819,0.0001432354,0.005962742],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006993933,"threshold_uncertainty_score":0.0369879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04191846451408229,"score_gpt":0.3783378437581784,"score_spread":0.3364193792440962,"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."}}