{"id":"W2946762441","doi":"10.1007/978-3-030-19823-7_37","title":"Learning Automata-Based Solutions to the Single Elevator Problem","year":2019,"lang":"en","type":"book-chapter","venue":"IFIP advances in information and communication technology","topic":"Elevator Systems and Control","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Elevator; Benchmark (surveying); Computer science; Learning automata; Scheme (mathematics); Convergence (economics); Automaton; Mathematical optimization; Scheduling (production processes); Field (mathematics); Artificial intelligence; Mathematics; Engineering","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":[0.0005071691,0.0006555854,0.000836889,0.0004201122,0.0004177335,0.0009016396,0.001573463,0.001579881,0.005853849],"category_scores_gemma":[0.003325006,0.0004300228,0.0007572013,0.0004832372,0.0007668805,0.0010617,0.001403356,0.001544639,0.0006614169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006443528,"about_ca_system_score_gemma":0.0009245711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004026112,"about_ca_topic_score_gemma":0.00653073,"domain_scores_codex":[0.9997547,0.00006715217,0.00001983186,0.0000742902,0.00004163727,0.0000424525],"domain_scores_gemma":[0.9984471,0.001197867,0.00007860807,0.00009743268,0.0001348087,0.00004421489],"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":[0.00005264673,0.00003646207,0.0002274299,0.00007218844,0.00002508263,0.00002770808,0.00006018228,0.9226741,0.0007588341,0.02258467,0.001749969,0.05173065],"study_design_scores_gemma":[0.000009407038,0.00001170371,0.00002588361,0.000004698039,0.000003242876,0.000004913544,0.000007894838,0.9833944,0.0001374336,0.01614502,0.0002527002,0.000002690111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03262323,0.0004449918,0.9552742,0.0004117288,0.0001120644,0.00004827879,0.0001359625,0.0005929827,0.01035667],"genre_scores_gemma":[0.6990193,0.0004939766,0.2869562,0.0002191976,0.0001262388,0.0002653161,0.0004425219,0.0001695081,0.01230775],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005853849,"threshold_uncertainty_score":0.01958311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006153271209208703,"score_gpt":0.200120828089534,"score_spread":0.1939675568803254,"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."}}