{"id":"W2143031849","doi":"","title":"Linear approximations for factored markov decision processes","year":2005,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Markov decision process; Mathematical optimization; Computer science; Representation (politics); Linear programming; Dynamic programming; Algorithm; Markov process; 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":[],"consensus_categories":[],"category_scores_codex":[0.0001403692,0.0002114855,0.0003122199,0.0002931533,0.0003047952,0.0000546136,0.0009609349,0.0002166493,0.00005567397],"category_scores_gemma":[0.00008465989,0.0002272396,0.0001518698,0.0003309735,0.00002849685,0.0004167341,0.00008326206,0.0002064058,0.00003098138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000395219,"about_ca_system_score_gemma":0.000176996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001626003,"about_ca_topic_score_gemma":0.00723211,"domain_scores_codex":[0.9988551,0.00002960759,0.0001346288,0.0004457271,0.0002987326,0.0002362166],"domain_scores_gemma":[0.9987315,0.0001381898,0.0002783052,0.0003838015,0.0003828678,0.00008536445],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004560431,0.0005971135,0.0003990516,0.003750512,0.0003096848,0.00002656616,0.2838148,0.002324013,0.0009522017,0.003325191,0.01223707,0.6918078],"study_design_scores_gemma":[0.007421163,0.001536259,0.01212479,0.002565002,0.000543105,0.00002191491,0.1342127,0.7257434,0.007050043,0.005966112,0.09886181,0.003953679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6686102,0.0003301508,0.3241239,0.002384701,0.001094809,0.001199479,0.000153147,0.0005361428,0.001567392],"genre_scores_gemma":[0.01067035,0.00009711033,0.5111777,0.0000160304,0.000132273,0.000002196135,0.0006453237,0.00002705805,0.477232],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7234194,"threshold_uncertainty_score":0.9266559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009836518629011503,"score_gpt":0.231340231597195,"score_spread":0.2215037129681835,"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."}}