{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008204631,0.0007075911,0.0007585984,0.000362731,0.0003335871,0.0004997699,0.001019985,0.000753978,0.001374259],"category_scores_gemma":[0.001760362,0.0003387304,0.0003694443,0.0003481459,0.0006390166,0.0007139814,0.0007726692,0.001161486,0.0001466372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001180861,"about_ca_system_score_gemma":0.001269857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0209088,"about_ca_topic_score_gemma":0.01891571,"domain_scores_codex":[0.9997862,0.00005456934,0.000009618456,0.00004394381,0.0000450975,0.00006057477],"domain_scores_gemma":[0.9993957,0.0003255871,0.00007077469,0.00002813706,0.00012817,0.00005160004],"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":[0.00002789584,0.00002435584,0.0004921688,0.00001668898,0.00001543596,0.00002932445,0.00001571296,0.9849482,0.0004412486,0.001705411,0.0003748644,0.01190869],"study_design_scores_gemma":[0.000001502436,0.000003828297,0.00002035253,7.558081e-7,0.000001231821,0.000001648326,0.000001079157,0.9995897,0.00004147111,0.0003071899,0.00003036818,7.78764e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08925364,0.000748709,0.9033977,0.0004754711,0.0001120469,0.00006755793,0.00006475906,0.0007476395,0.005132488],"genre_scores_gemma":[0.9630142,0.0001396758,0.03449416,0.0001504701,0.00002009621,0.00005548153,0.00006434749,0.00004244479,0.002019101],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0209088,"threshold_uncertainty_score":0.04157418,"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."}}