{"id":"W2952303324","doi":"10.48550/arxiv.1810.06681","title":"Learn Fast, Forget Slow: Safe Predictive Learning Control for Systems with Unknown and Changing Dynamics Performing Repetitive Tasks","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Model predictive control; Computer science; Term (time); Process (computing); Gaussian process; Bayesian optimization; Regression; Bayesian probability; Artificial intelligence; Control theory (sociology); Controller (irrigation); Robot; Machine learning; Gaussian; Control (management); 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":[0.001396712,0.001402096,0.001088933,0.0004293169,0.0004658501,0.0007956242,0.001361832,0.0009039104,0.001457171],"category_scores_gemma":[0.003162375,0.0006386312,0.0005221058,0.0003863743,0.001263701,0.0009136613,0.001591843,0.001651268,0.0003603582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006916499,"about_ca_system_score_gemma":0.001295239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01248281,"about_ca_topic_score_gemma":0.01054738,"domain_scores_codex":[0.9995012,0.0001003262,0.00002205472,0.0001154331,0.0001748043,0.00008618703],"domain_scores_gemma":[0.9986878,0.000605058,0.0002401251,0.0001276585,0.0002702625,0.00006905961],"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":[0.00005174549,0.00002378751,0.0002147505,0.00004472762,0.00002041205,0.00003548837,0.00005443867,0.9745442,0.001139827,0.002165556,0.0004048285,0.02130034],"study_design_scores_gemma":[0.000005149352,0.00002243373,0.00004074119,0.000002397294,0.000002145726,0.000002817277,0.000002491957,0.9988085,0.0001851276,0.0008180965,0.0001078645,0.00000230178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01883115,0.0002011493,0.9788792,0.0001504267,0.00003234126,0.00002578214,0.00002979789,0.0005328673,0.001317317],"genre_scores_gemma":[0.9270577,0.0001706898,0.06901821,0.0001586739,0.00005689739,0.0001541312,0.0001165154,0.0001362382,0.003130907],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01248281,"threshold_uncertainty_score":0.02482033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01171291358641694,"score_gpt":0.1518006346798519,"score_spread":0.1400877210934349,"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."}}