{"id":"W4319878926","doi":"10.1109/tits.2023.3242997","title":"Joint Secure Offloading and Resource Allocation for Vehicular Edge Computing Network: A Multi-Agent Deep Reinforcement Learning Approach","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":222,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Key Research and Development Program of China; Japan Society for the Promotion of Science; National Natural Science Foundation of China","keywords":"Reinforcement learning; Computer science; Joint (building); Resource allocation; Distributed computing; Artificial intelligence; Edge computing; Enhanced Data Rates for GSM Evolution; Vehicular ad hoc network; Computer network; Engineering; Wireless ad hoc network; Wireless; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006510976,0.0007311344,0.0008347744,0.0002507883,0.0003691912,0.0006402353,0.0009664416,0.0008610241,0.0008312327],"category_scores_gemma":[0.001139748,0.000317543,0.0003579833,0.0002346348,0.000807639,0.0007543899,0.0009553773,0.001070528,0.00009573691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008137806,"about_ca_system_score_gemma":0.001163073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007695902,"about_ca_topic_score_gemma":0.00572774,"domain_scores_codex":[0.9996935,0.00007925008,0.00001274512,0.00006626602,0.00005848837,0.00008976298],"domain_scores_gemma":[0.9995401,0.000226698,0.00006817486,0.00002694225,0.00009337123,0.00004475235],"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":[0.00003977017,0.00003558905,0.0005041261,0.00002169418,0.00002265278,0.00005360442,0.00003291085,0.9794363,0.001183877,0.004644765,0.0003226188,0.01370198],"study_design_scores_gemma":[0.000001968943,0.000007350178,0.00001788232,7.561843e-7,0.000001684154,0.000002281794,0.000002139083,0.9992846,0.00008323353,0.0005622564,0.00003481652,0.000001073962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0674951,0.0003939149,0.9283198,0.000415171,0.00004400895,0.00003601136,0.00001893031,0.0001752191,0.003101782],"genre_scores_gemma":[0.9761513,0.0001201083,0.02180735,0.00009986215,0.00001874128,0.00004314164,0.00002052127,0.0000134651,0.001725454],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007695902,"threshold_uncertainty_score":0.01530224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06202309640502126,"score_gpt":0.2770066687207837,"score_spread":0.2149835723157625,"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."}}