{"id":"W2051144240","doi":"10.1117/12.538812","title":"<title>Efficient QoS provisioning for adaptive multimedia in mobile communication networks by reinforcement learning</title>","year":2003,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Reinforcement learning; Quality of service; Computer network; Markov decision process; Call Admission Control; Provisioning; Bandwidth allocation; Bandwidth (computing); Wireless network; Wireless; Handover; Multimedia; Markov process; Distributed computing; Artificial intelligence; 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":[],"consensus_categories":[],"category_scores_codex":[0.0008387823,0.000161212,0.0002044034,0.00008472066,0.00009427316,0.00009855153,0.001220241,0.0001265224,0.00002377638],"category_scores_gemma":[0.0003724126,0.0001452595,0.000195004,0.0003356824,0.0001124338,0.0002220843,0.0002530039,0.0003336478,0.000006441494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001774282,"about_ca_system_score_gemma":0.00004146491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001964042,"about_ca_topic_score_gemma":9.727475e-8,"domain_scores_codex":[0.9985886,1.212967e-7,0.00041789,0.0002500112,0.0004460774,0.0002973044],"domain_scores_gemma":[0.9985204,0.0002571692,0.0002240839,0.0001171091,0.0008096513,0.00007159662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002843856,0.0001348068,0.00003925926,0.00009521017,0.0001073745,3.47336e-8,0.0002811445,0.03760106,0.01872158,0.9132411,0.02524049,0.004509519],"study_design_scores_gemma":[0.0004339708,0.0001276111,0.00002532278,0.0001400566,0.00001026584,0.000001534477,0.0001332266,0.9420667,0.00353256,0.0002428479,0.05313075,0.0001551269],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8407398,0.00346395,0.06755518,0.002173734,0.000769885,0.004284906,0.00002231802,0.000370464,0.08061978],"genre_scores_gemma":[0.7086233,0.0006087408,0.2880049,0.00006087956,0.0001558403,0.0007227617,0.00002245908,0.00005801779,0.001743075],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9129983,"threshold_uncertainty_score":0.5923509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01251680454415087,"score_gpt":0.2461388659516576,"score_spread":0.2336220614075068,"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."}}