{"id":"W4315488874","doi":"10.1109/icarcv57592.2022.10004318","title":"Data Transmission Resilience to Cyber-attacks on Heterogeneous Multi-agent Deep Reinforcement Learning Systems","year":2022,"lang":"en","type":"article","venue":"2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV)","topic":"Ecosystem dynamics and resilience","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Reinforcement learning; Computer science; Resilience (materials science); Data transmission; Transmission (telecommunications); Adversarial system; Controller (irrigation); Artificial intelligence; Reinforcement; Computer network; Engineering; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009143019,0.0002821771,0.0002876839,0.0001707367,0.0008301864,0.00026751,0.001155945,0.000067486,0.001392001],"category_scores_gemma":[0.0000894504,0.0002526498,0.00006171764,0.0002279145,0.00007540728,0.0002268188,0.0007415881,0.0003666017,0.0001438835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003864339,"about_ca_system_score_gemma":0.00004545934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001685534,"about_ca_topic_score_gemma":0.00008257268,"domain_scores_codex":[0.9963462,0.0002827589,0.0006575001,0.0009072687,0.001451187,0.000355085],"domain_scores_gemma":[0.9986191,0.0001410747,0.0003031034,0.0005829808,0.00008419211,0.0002694974],"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":[0.0001024388,0.0001510418,0.001345653,0.00001251808,0.00002763114,0.00001585746,0.0002433774,0.9873708,0.001242561,0.004136145,0.0005630828,0.004788884],"study_design_scores_gemma":[0.0009084925,0.0006520671,0.00455318,0.00008616674,0.00001659271,0.00002070328,0.0002974484,0.9859019,0.00001735879,0.00004530393,0.007209858,0.000290952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07411462,0.000212643,0.9002038,0.008490275,0.002390771,0.002772308,0.0003016928,0.0002684906,0.01124546],"genre_scores_gemma":[0.9958104,0.000122129,0.0009375372,0.0005503717,0.00003777584,0.00009935326,0.0002072171,0.00002450296,0.002210661],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9216958,"threshold_uncertainty_score":0.9999925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0261775429513091,"score_gpt":0.3024219929480291,"score_spread":0.27624444999672,"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."}}