{"id":"W2801647199","doi":"","title":"A Risk-Constrained Markov Decision Process Approach to Scheduling Mixed-Criticality Job Sets","year":2016,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Real-Time Systems Scheduling","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vancouver Biotech (Canada)","funders":"","keywords":"Markov decision process; Probabilistic logic; Computer science; Scheduling (production processes); Mathematical optimization; Markov process; Criticality; Markov chain; Dynamic priority scheduling; Mixed criticality; Job shop scheduling; Mathematics; Schedule; Artificial intelligence; Machine learning","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":[0.005679699,0.001776561,0.003685518,0.001757225,0.0010868,0.002735973,0.003467338,0.002649178,0.007664917],"category_scores_gemma":[0.01130466,0.002496347,0.002064904,0.001712387,0.002007205,0.002126217,0.002359119,0.00384393,0.0005280788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003500081,"about_ca_system_score_gemma":0.004821952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01959958,"about_ca_topic_score_gemma":0.01604706,"domain_scores_codex":[0.9975326,0.001040938,0.0001055575,0.0004166132,0.0004879432,0.0004162963],"domain_scores_gemma":[0.9885843,0.009306327,0.0005839188,0.0001980082,0.0007296822,0.0005977268],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005216796,0.00003552006,0.0001329129,0.00005015757,0.0000348698,0.00003856049,0.00003161113,0.9783193,0.0002047709,0.01762477,0.000426429,0.003048874],"study_design_scores_gemma":[0.000008006486,0.00001176888,0.00003017729,0.000004756116,0.000006826447,0.000004158871,0.000004204713,0.9907491,0.00003526124,0.009036263,0.0001041824,0.000005444605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01349308,0.0004809764,0.9820949,0.0006025434,0.0001343773,0.0001121293,0.0001496906,0.0001708787,0.002761327],"genre_scores_gemma":[0.7578077,0.001190037,0.2250936,0.0004286751,0.0005078726,0.0005867576,0.0004864205,0.0003219821,0.01357686],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01959958,"threshold_uncertainty_score":0.03897101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01568084722786472,"score_gpt":0.2597981633115673,"score_spread":0.2441173160837026,"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."}}