{"id":"W2571456196","doi":"10.48550/arxiv.1103.4342","title":"MDP Optimal Control under Temporal Logic Constraints","year":2011,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Formal Methods in Verification","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Air Force Office of Scientific Research; Army Research Office; Office of Naval Research; Multidisciplinary University Research Initiative; National Science Foundation","keywords":"Markov decision process; Temporal logic; Dynamic programming; Linear temporal logic; Mathematical optimization; Computer science; Set (abstract data type); Optimal control; Proposition; Control (management); Process (computing); Partially observable Markov decision process; Markov process; Mathematics; Artificial intelligence; Algorithm; Theoretical computer science","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":[0.0003764763,0.0001395003,0.00014842,0.0001153523,0.000118749,0.00003301575,0.0009743356,0.00009712502,0.0001566926],"category_scores_gemma":[0.00003617051,0.0001483128,0.00007981573,0.0004321023,0.0002929948,0.0007304947,0.0001434883,0.0001543444,0.0002520665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007271831,"about_ca_system_score_gemma":0.00005878095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003699265,"about_ca_topic_score_gemma":0.000003428047,"domain_scores_codex":[0.9988767,0.0001554856,0.0001580915,0.000462644,0.0000623544,0.0002847383],"domain_scores_gemma":[0.9990047,0.00004681841,0.0001298183,0.0005954908,0.00009271751,0.0001304738],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000269345,0.00006376037,0.006439771,0.000003917756,0.00001952515,0.00007803374,0.0001320278,0.002770593,0.0000924693,0.989524,0.00003683087,0.0008121065],"study_design_scores_gemma":[0.002460956,0.0003996541,0.03376106,0.00001828263,0.00006137318,0.00006278857,0.0004602158,0.8576227,0.002084097,0.1017996,0.0004856129,0.0007836938],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07656821,0.000007456574,0.9096505,0.00002561145,0.0002434727,0.0001304815,0.000002617832,0.0001902529,0.01318139],"genre_scores_gemma":[0.8485093,0.000003489355,0.1509309,0.0002149492,0.00001643185,3.931061e-7,8.601955e-7,0.000005666504,0.0003180867],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8877245,"threshold_uncertainty_score":0.6048021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1593707195394043,"score_gpt":0.2079665117255625,"score_spread":0.04859579218615823,"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."}}