{"id":"W4400526139","doi":"10.1109/tce.2024.3426483","title":"GCN-Based Multi-Agent Deep Reinforcement Learning for Dynamic Service Function Chain Deployment in IoT","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Consumer Electronics","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"National Natural Science Foundation of China","keywords":"Reinforcement learning; Computer science; Software deployment; Chain (unit); Function (biology); Internet of Things; Service (business); Computer network; Distributed computing; Artificial intelligence; Computer security; Software engineering; Business","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"],"consensus_categories":[],"category_scores_codex":[0.0004465629,0.0002727116,0.0002045036,0.0004262547,0.0003240501,0.0001899233,0.0003310433,0.0001173053,0.000007822631],"category_scores_gemma":[0.000004885209,0.0002901132,0.0001542597,0.0008154779,0.00001775231,0.0001588494,0.000004682202,0.0006255845,0.00007798443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007809225,"about_ca_system_score_gemma":0.0003882381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006327477,"about_ca_topic_score_gemma":0.0005222643,"domain_scores_codex":[0.9979509,0.00008590645,0.0003970873,0.0005959812,0.0002651363,0.0007049486],"domain_scores_gemma":[0.9991648,0.0002590738,0.00006169751,0.0003332074,0.00008975195,0.00009143926],"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":[0.00006268368,0.0001494134,0.00001272307,0.0001339751,0.00009283025,0.00000637822,0.0004731626,0.7836211,0.001767041,0.0001561351,0.00004762191,0.213477],"study_design_scores_gemma":[0.0009191927,0.000340824,0.00003117654,0.0001087125,0.00004964993,0.000005800835,0.0000156388,0.9795543,0.005183645,0.00006557559,0.0134256,0.0002999159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01030586,0.001125342,0.9817704,0.0007569434,0.004909185,0.0006130824,3.615468e-7,0.000499565,0.00001926983],"genre_scores_gemma":[0.9928131,0.000074569,0.005832303,0.0005721008,0.00007216149,0.0002344845,0.000008117662,0.00004561555,0.0003475418],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9825072,"threshold_uncertainty_score":0.9999551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01685641336921461,"score_gpt":0.2587572105760929,"score_spread":0.2419007972068783,"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."}}