{"id":"W2964138387","doi":"","title":"Approximate linear programming for first-order MDPs","year":2005,"lang":"en","type":"article","venue":"","topic":"Elevator Systems and Control","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Markov decision process; Computer science; Linear programming; Bellman equation; Mathematical optimization; Algorithm; Set (abstract data type); Scheduling (production processes); Domain (mathematical analysis); 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":[0.00008057852,0.00009331264,0.0001134447,0.00002323472,0.00004936086,0.00002593724,0.00006698244,0.00004936797,0.00004423353],"category_scores_gemma":[0.000009178447,0.00007941607,0.00004740737,0.0000627556,0.000005691838,0.00007345984,0.000006436245,0.00004422596,0.00005469117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002021166,"about_ca_system_score_gemma":0.000004215713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007022711,"about_ca_topic_score_gemma":0.0000741253,"domain_scores_codex":[0.9994649,0.000002126482,0.0001486537,0.00009402605,0.00005924492,0.0002310319],"domain_scores_gemma":[0.9997656,0.00002150805,0.00001102426,0.0001232466,0.00003329947,0.00004530361],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003832117,0.000178522,0.001473179,0.001537991,0.0004080799,0.000004380854,0.001457601,0.09369022,0.005748219,0.0346937,0.02981814,0.8309516],"study_design_scores_gemma":[0.0003553499,0.00001460517,0.00001111673,0.000009878328,0.000006119374,0.000001613457,0.00002812553,0.4782787,0.0008538581,0.00001448452,0.5203214,0.0001048459],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04294655,0.0013344,0.9337496,0.0007999788,0.0005397976,0.001637997,0.000009152477,0.00200627,0.01697623],"genre_scores_gemma":[0.9350199,0.00000763835,0.06142079,0.00007289513,0.0006715456,0.0003629271,0.000004727641,0.00004794466,0.002391602],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8920734,"threshold_uncertainty_score":0.3238493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00835195303255513,"score_gpt":0.2103922993557825,"score_spread":0.2020403463232274,"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."}}