{"id":"W2120518702","doi":"10.1109/cdc.2009.5400796","title":"Parametric regret in uncertain Markov decision processes","year":2009,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Regret; Markov decision process; Parametric statistics; Computer science; Markov process; Markov chain; Decision theory; Mathematical optimization; Artificial intelligence; Machine learning; Mathematics; Statistics","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":[0.006446145,0.001502596,0.002153528,0.0007164666,0.0008351459,0.00280765,0.001573108,0.002466622,0.002599428],"category_scores_gemma":[0.02344496,0.0008656849,0.001085794,0.001204688,0.002986453,0.003197156,0.002095919,0.002708404,0.000317948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002851162,"about_ca_system_score_gemma":0.001562728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003342308,"about_ca_topic_score_gemma":0.001958867,"domain_scores_codex":[0.9954151,0.002483444,0.0001461397,0.0006267827,0.0007403016,0.0005881863],"domain_scores_gemma":[0.9821514,0.01514377,0.001429715,0.000499043,0.0003740876,0.0004020001],"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":[0.0001041377,0.00003088807,0.0006029356,0.00005998525,0.00004314017,0.0001382657,0.00005087324,0.8696224,0.0002712659,0.1239619,0.0004559289,0.004658336],"study_design_scores_gemma":[0.0000166208,0.00002486818,0.0001686411,0.00001055938,0.000009007655,0.00002273795,0.00001016054,0.8907984,0.0001262395,0.108608,0.000192738,0.00001194576],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06454159,0.001683519,0.9233491,0.002014619,0.00008360047,0.00006431529,0.0001721027,0.0002090274,0.007882112],"genre_scores_gemma":[0.951669,0.001204069,0.04235703,0.0002925264,0.0001552346,0.0001550325,0.000140522,0.0000503847,0.003976284],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006446145,"threshold_uncertainty_score":0.03409082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07366231025112745,"score_gpt":0.3976961143989476,"score_spread":0.3240338041478201,"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."}}