{"id":"W2377441786","doi":"","title":"Reinforcement learning algorithm based on general fuzzifiedcerebellar model articulation controller","year":2004,"lang":"en","type":"article","venue":"Systems engineering and electronics","topic":"Advanced Sensor and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"L'Alliance Boviteq","funders":"","keywords":"Cerebellar model articulation controller; Reinforcement learning; Computer science; Field (mathematics); Controller (irrigation); Articulation (sociology); Reinforcement; Control theory (sociology); Artificial intelligence; Algorithm; Control (management); Engineering; Mathematics","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":[0.0006902615,0.0006352118,0.0007839866,0.0003340559,0.0004271257,0.0007449824,0.001157259,0.001068533,0.002132451],"category_scores_gemma":[0.001381632,0.0001967444,0.0003773022,0.0002576215,0.0007716575,0.0005358393,0.0005606314,0.0008232268,0.0002778463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007727131,"about_ca_system_score_gemma":0.0009902187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008163496,"about_ca_topic_score_gemma":0.003334096,"domain_scores_codex":[0.9996861,0.00005683786,0.00001994272,0.00008516545,0.0001012582,0.00005066452],"domain_scores_gemma":[0.9995717,0.0001479116,0.00005595928,0.00003693371,0.0001557202,0.00003168183],"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":[0.0001790249,0.00006259584,0.0006669781,0.0001128286,0.00005763867,0.0001798468,0.0001613839,0.849589,0.01139561,0.02275174,0.001289455,0.1135538],"study_design_scores_gemma":[0.00003368161,0.0000497259,0.0001027437,0.000005133151,0.000006735898,0.00003161238,0.000003679882,0.9965315,0.0009919748,0.001670844,0.0005646472,0.000007707706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02089957,0.0001855677,0.9746553,0.0001400448,0.00007101554,0.00006874718,0.00001943081,0.000655488,0.003304792],"genre_scores_gemma":[0.8726187,0.0001712537,0.1220658,0.000105846,0.00004442946,0.0002317027,0.00007100721,0.00003196114,0.004659359],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008163496,"threshold_uncertainty_score":0.01623195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003740636381374708,"score_gpt":0.1735484543960202,"score_spread":0.1698078180146455,"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."}}