{"id":"W1526757356","doi":"10.1007/11424918_6","title":"Multiagent Systems Viewed as Distributed Scheduling Systems: Methodology and Experiments","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Distributed computing; Scheduling (production processes); Multi-agent system; Two-level scheduling; Fair-share scheduling; Artificial intelligence; Mathematical optimization; Computer network; Mathematics","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":[0.006125053,0.0008353509,0.001045021,0.0007709824,0.000692095,0.001210418,0.001325751,0.0009632027,0.002991411],"category_scores_gemma":[0.0166574,0.0003765018,0.0005265421,0.001357167,0.001272953,0.001716846,0.0008455874,0.001421864,0.000375788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001261988,"about_ca_system_score_gemma":0.001023466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003150277,"about_ca_topic_score_gemma":0.001277225,"domain_scores_codex":[0.9969493,0.002222004,0.0001082075,0.000210673,0.0003525264,0.000157446],"domain_scores_gemma":[0.9682443,0.02532113,0.001253714,0.003037539,0.001660435,0.0004828515],"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":[0.006644407,0.007763471,0.005127281,0.001098642,0.0003239614,0.0001595874,0.001075117,0.807084,0.02386483,0.04498211,0.003726287,0.09815024],"study_design_scores_gemma":[0.0006275112,0.002030327,0.001563153,0.00003800044,0.000106623,0.00003961638,0.000347353,0.9681588,0.01009641,0.01601947,0.0009326083,0.00004016388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7819811,0.001055339,0.2064518,0.0002809789,0.0001932308,0.000619893,0.0003553167,0.0005924954,0.008469834],"genre_scores_gemma":[0.9398771,0.0003664274,0.05744851,0.000040726,0.00002913016,0.0003802184,0.0002008682,0.00008055079,0.001576546],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006125053,"threshold_uncertainty_score":0.03239274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04190027647720646,"score_gpt":0.2862282968172445,"score_spread":0.244328020340038,"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."}}