{"id":"W4389215222","doi":"10.1016/j.automatica.2023.111417","title":"Resilient and constrained consensus against adversarial attacks: A distributed MPC framework","year":2023,"lang":"en","type":"article","venue":"Automatica","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"Japan Society for the Promotion of Science; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Robustness (evolution); Subsequence; Consensus; Adversarial system; Convergence (economics); Resilience (materials science); Distributed computing; Mathematical optimization; Multi-agent system; Mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005834079,0.0002785201,0.0004292915,0.0001562188,0.0002325577,0.0003227106,0.0007025134,0.0001872807,0.0000164196],"category_scores_gemma":[0.00124074,0.0002646181,0.0001019671,0.000995527,0.0002202607,0.0001546179,0.0003718143,0.0002214784,0.0005605464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008210477,"about_ca_system_score_gemma":0.0001567178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001690844,"about_ca_topic_score_gemma":0.000003724307,"domain_scores_codex":[0.9974186,0.0002195731,0.0005874929,0.0006114637,0.0005089446,0.0006538957],"domain_scores_gemma":[0.9973744,0.00111824,0.0002022766,0.0009068612,0.0001113529,0.0002868744],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001822609,0.000701294,0.005849076,0.0007411457,0.001413577,0.003039357,0.007683638,0.003958655,0.007811973,0.6781546,0.2063885,0.08407585],"study_design_scores_gemma":[0.00237679,0.0001064655,0.01397048,0.000290449,0.00004102022,0.0000525681,0.000373065,0.9680471,0.0001772406,0.006030526,0.007973698,0.0005606444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2959793,0.0001899486,0.6737654,0.01502713,0.00284438,0.001916362,0.0007421023,0.006058687,0.003476726],"genre_scores_gemma":[0.9881807,0.000009415748,0.01115148,0.0002834768,0.00009983183,0.00006277805,0.00009692073,0.0000184502,0.00009693091],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9640884,"threshold_uncertainty_score":0.9999806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01537296729139619,"score_gpt":0.2615588138102654,"score_spread":0.2461858465188692,"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."}}