{"id":"W3157126773","doi":"10.1111/itor.12989","title":"An extended ϵ‐constraint method for a multiobjective finite‐horizon Markov decision process","year":2021,"lang":"en","type":"article","venue":"International Transactions in Operational Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kwantlen Polytechnic University; University of British Columbia","funders":"","keywords":"Mathematical optimization; Markov decision process; Computer science; Pareto principle; Constraint (computer-aided design); Scheduling (production processes); Markov process; Selection (genetic algorithm); Class (philosophy); Mathematics; Artificial intelligence","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":[0.002162586,0.0009469959,0.001177507,0.0008113852,0.0005217529,0.000944188,0.001487585,0.00108962,0.003962887],"category_scores_gemma":[0.003292707,0.0005927258,0.001132147,0.001054964,0.0007127139,0.000864504,0.001095461,0.001544826,0.0003605514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001067034,"about_ca_system_score_gemma":0.002324373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01196929,"about_ca_topic_score_gemma":0.006341706,"domain_scores_codex":[0.9992051,0.0003231482,0.0000398919,0.000102122,0.0002581363,0.00007161919],"domain_scores_gemma":[0.9977407,0.001587952,0.000145307,0.00006185748,0.0003766199,0.00008752253],"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":[0.00003828394,0.00002072868,0.0001398829,0.00006146909,0.00002716385,0.0000589054,0.00001966719,0.9690878,0.0008237833,0.009620579,0.0003555796,0.01974626],"study_design_scores_gemma":[0.000003979526,0.000006396384,0.00001239634,0.000003869081,0.000001713912,0.000003686712,0.000001198551,0.998806,0.0001110906,0.0008329115,0.0002146351,0.000002041855],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002695758,0.00007587404,0.9960794,0.00005200061,0.00002136874,0.00003860335,0.00002665401,0.00005640356,0.0009540567],"genre_scores_gemma":[0.2981395,0.0003372928,0.6970143,0.0001271784,0.00007081661,0.0006237234,0.0001951077,0.000113427,0.003378577],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01196929,"threshold_uncertainty_score":0.02379924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05930180064658388,"score_gpt":0.4521147720902913,"score_spread":0.3928129714437075,"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."}}