{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001975376,0.0009531801,0.0009371807,0.0005585483,0.0005668947,0.000971604,0.0008920847,0.0008328735,0.002724441],"category_scores_gemma":[0.00543829,0.0005857163,0.0007097783,0.0004180912,0.00173758,0.001106765,0.001250399,0.001454886,0.0002038631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001946248,"about_ca_system_score_gemma":0.002639946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005713858,"about_ca_topic_score_gemma":0.004854723,"domain_scores_codex":[0.9989416,0.0003278081,0.00005418956,0.0002506386,0.0002861042,0.0001396455],"domain_scores_gemma":[0.9969388,0.002460208,0.0002208702,0.0001016823,0.000202548,0.00007602892],"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":[0.00005952038,0.0000297963,0.0001921543,0.00008034895,0.00002038942,0.00008121502,0.00005932226,0.9184317,0.001135052,0.06620917,0.0004480097,0.01325337],"study_design_scores_gemma":[0.00002362115,0.00001485731,0.00001999023,0.000007517034,0.000005204445,0.00000695671,0.000006299685,0.9777588,0.0006435811,0.02092093,0.0005886236,0.000003671386],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01166947,0.0001850452,0.984448,0.0002582917,0.00002811381,0.00007271667,0.00006052474,0.0003214869,0.002956305],"genre_scores_gemma":[0.6023376,0.0002808888,0.3927191,0.000180249,0.00004131664,0.0004288764,0.0001905704,0.0001589465,0.003662467],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005713858,"threshold_uncertainty_score":0.01412112,"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."}}