{"id":"W3170969450","doi":"10.1109/tpwrs.2021.3088376","title":"Real-Time Resilience Optimization Combining an AI Agent With Online Hard Optimization","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Infrastructure Resilience and Vulnerability Analysis","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Control reconfiguration; Resilience (materials science); Convergence (economics); Computer science; Interdependence; Fault (geology); Electric power system; Distributed computing; Node (physics); Optimization problem; Multi-agent system; Electricity; Reliability engineering; Mathematical optimization; Engineering; Power (physics); Artificial intelligence; Algorithm; Embedded system","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":[0.0009871348,0.001251099,0.0009773485,0.0005299109,0.0004708192,0.001135217,0.001011044,0.001143816,0.002060653],"category_scores_gemma":[0.00249455,0.0004629946,0.0005307655,0.0003789873,0.0009416034,0.000809762,0.00133937,0.001399634,0.0002696066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006504494,"about_ca_system_score_gemma":0.001268161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005548018,"about_ca_topic_score_gemma":0.003645568,"domain_scores_codex":[0.9995925,0.0001494904,0.00001929312,0.0000731775,0.00008975417,0.00007587842],"domain_scores_gemma":[0.9986516,0.0008558704,0.0001535557,0.00008552628,0.0001687531,0.00008464071],"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":[0.00002810905,0.00002472509,0.0001520792,0.00001508175,0.00001252586,0.00002297286,0.00001357598,0.9918499,0.0003837579,0.001488848,0.0001365537,0.005871922],"study_design_scores_gemma":[0.000003831767,0.00001089606,0.00001503341,0.000001125247,0.000001697255,0.000002244444,0.000002369667,0.9994115,0.00009308592,0.0003890253,0.0000679375,0.000001198113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05177543,0.0002337608,0.9384413,0.0004538154,0.00008993746,0.0001215214,0.00003425613,0.0006572179,0.008192781],"genre_scores_gemma":[0.8907846,0.00009986822,0.1053639,0.0001568743,0.0000507408,0.0002173987,0.00005222214,0.00006048222,0.003213809],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005548018,"threshold_uncertainty_score":0.01103145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007875328487512812,"score_gpt":0.2253656502985479,"score_spread":0.2174903218110351,"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."}}