{"id":"W4244440630","doi":"10.1109/cac53003.2021.9727718","title":"Value Iteration-based Zero-sum Neuro-optimal Control of Modular and Reconfigurable Robots via Adaptive Dynamic Programming","year":2021,"lang":"en","type":"article","venue":"2021 China Automation Congress (CAC)","topic":"Adaptive Dynamic Programming Control","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Dynamic programming; Control theory (sociology); Optimal control; Bellman equation; Convergence (economics); Modular design; Artificial neural network; Mathematics; Iterated function; Lyapunov function; Dimension (graph theory); Adaptive control; Mathematical optimization; Computer science; Control (management); Nonlinear system; Artificial intelligence; Mathematical analysis","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":[0.0004094515,0.0006645027,0.0006152059,0.0002408256,0.0003211045,0.0006521331,0.0007043516,0.0005195579,0.0007499816],"category_scores_gemma":[0.0005887676,0.0003147295,0.0003965691,0.0003257463,0.0006408023,0.0003829216,0.0007346446,0.0006055624,0.0001084259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004216243,"about_ca_system_score_gemma":0.0006566197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003663844,"about_ca_topic_score_gemma":0.002610639,"domain_scores_codex":[0.9998285,0.00004455597,0.000007952263,0.00004334794,0.00004654771,0.00002905823],"domain_scores_gemma":[0.9998171,0.00007596318,0.00004307563,0.000009515109,0.00004208999,0.00001240685],"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":[0.00003159995,0.00001865101,0.0001778701,0.00004219787,0.00002354741,0.00005498981,0.0000504895,0.958991,0.00302006,0.006502786,0.0003369477,0.03074986],"study_design_scores_gemma":[0.000003518098,0.00002451868,0.00003412178,0.000001818946,0.000002141608,0.000005702377,0.000002848534,0.9988005,0.0002411857,0.0007459265,0.0001352464,0.000002398077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02576452,0.0002221989,0.9700409,0.00009079291,0.00003301111,0.00002583771,0.00001111264,0.0001247735,0.003686908],"genre_scores_gemma":[0.929603,0.0001644977,0.06706146,0.00004643298,0.00002153413,0.0001482693,0.00002473065,0.00002034662,0.002909732],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003663844,"threshold_uncertainty_score":0.007284999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005959237061519206,"score_gpt":0.2225785552987345,"score_spread":0.2166193182372153,"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."}}