{"id":"W2948967385","doi":"10.1109/access.2019.2920662","title":"High-Reliability Multi-Agent Q-Learning-Based Scheduling for D2D Microgrid Communications","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Age of Information Optimization","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Division of Computer and Network Systems; Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Computer science; Microgrid; Computer network; 3rd Generation Partnership Project 2; Smart grid; Quality of service; Real-time computing; Scheduling (production processes); Latency (audio); Distributed computing; Telecommunications link; Mathematical optimization; Engineering; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001248693,0.0005271575,0.000711126,0.0002976391,0.0005731428,0.0005536816,0.001154019,0.0005434129,0.001136804],"category_scores_gemma":[0.002390894,0.0002470159,0.0002811755,0.0003279737,0.0006426806,0.000600216,0.0007656398,0.0006173571,0.0001957772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008325883,"about_ca_system_score_gemma":0.001194609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005030986,"about_ca_topic_score_gemma":0.003748703,"domain_scores_codex":[0.9995828,0.0001643453,0.00002005765,0.00007637521,0.00009307197,0.00006339317],"domain_scores_gemma":[0.9988382,0.0006164227,0.0001693222,0.00007582385,0.0002244569,0.00007580945],"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":[0.00005256681,0.00004594146,0.0004340602,0.000020115,0.00001559809,0.00002526116,0.00002982517,0.9726282,0.001002511,0.003321111,0.0003933648,0.02203135],"study_design_scores_gemma":[0.000007067771,0.00001422846,0.00002996914,7.497127e-7,0.000001433324,0.000003465797,0.000002242087,0.9991583,0.0001303775,0.0005445916,0.0001062554,0.000001286486],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02847006,0.0001212643,0.9693201,0.0001314442,0.00003569515,0.00004963197,0.00001537626,0.0001966085,0.001659728],"genre_scores_gemma":[0.9179143,0.00007947855,0.08056362,0.00007966797,0.00002861514,0.00009515104,0.00003144372,0.00002152856,0.001186054],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005030986,"threshold_uncertainty_score":0.01000339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0345348606107653,"score_gpt":0.3128113740015508,"score_spread":0.2782765133907855,"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."}}