{"id":"W7125585396","doi":"10.1109/iciteics64870.2025.11341125","title":"Adaptive Scheduling and Managing Resources in Changing Industrial Settings: Deep Reinforcement Learning for the Internet of Things","year":2025,"lang":"","type":"article","venue":"","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Reinforcement learning; Scheduling (production processes); Provisioning; Process (computing); Artificial neural network; Curse of dimensionality; Intelligent agent; Factory (object-oriented programming); Resource (disambiguation)","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":[0.0008024042,0.0004700137,0.0005401403,0.0001694281,0.0002323388,0.0005048744,0.0006181361,0.0005813199,0.0007106389],"category_scores_gemma":[0.002139129,0.0002328659,0.0003290492,0.0001734555,0.0005673222,0.0006010824,0.0005993471,0.001048134,0.00008229884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006562559,"about_ca_system_score_gemma":0.0007245716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006797854,"about_ca_topic_score_gemma":0.005511477,"domain_scores_codex":[0.9998121,0.00006865246,0.000009371153,0.000039511,0.0000297747,0.0000405711],"domain_scores_gemma":[0.9991929,0.0005313828,0.00009033844,0.00003293593,0.00009283314,0.00005967898],"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":[0.00003546751,0.00004083824,0.0006656415,0.00002186914,0.00001628292,0.00002720568,0.00001917868,0.9865289,0.0006307196,0.001461514,0.0003366261,0.0102158],"study_design_scores_gemma":[0.000002503949,0.000009560804,0.00006408781,0.000001333869,0.000001567798,0.000001661598,0.000002382027,0.9991467,0.0000790273,0.0006318192,0.00005804734,0.000001250697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2403355,0.001503133,0.7512369,0.00132051,0.00016085,0.00006943637,0.00008492833,0.0006113196,0.004677475],"genre_scores_gemma":[0.9792339,0.0001515782,0.01955919,0.0001316002,0.00001713724,0.00003185935,0.00004275304,0.00001739314,0.0008146188],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006797854,"threshold_uncertainty_score":0.01351655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02332855182249038,"score_gpt":0.2465733480167192,"score_spread":0.2232447961942288,"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."}}