{"id":"W2475215202","doi":"10.1145/2875441","title":"Multiagent Resource Allocation for Dynamic Task Arrivals with Preemption","year":2016,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Preemption; Computer science; Leverage (statistics); Task (project management); Distributed computing; Proxy (statistics); Resource (disambiguation); Resource allocation; Multi-agent system; Computer network; Artificial intelligence; Machine learning","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":[0.000213408,0.0001333811,0.0001486288,0.000394957,0.000193914,0.00006270034,0.0004375778,0.0001487975,0.000007482774],"category_scores_gemma":[0.00003431697,0.00008697102,0.00003041558,0.0003340826,0.00009273369,0.000181166,0.00001413773,0.00009197072,0.00002140393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007322496,"about_ca_system_score_gemma":0.0000288746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001382687,"about_ca_topic_score_gemma":0.00002545103,"domain_scores_codex":[0.9989232,0.00004243684,0.0002498116,0.0004091849,0.0001471558,0.0002282212],"domain_scores_gemma":[0.9988815,0.0001433221,0.00008895039,0.0006717173,0.0001497639,0.00006473627],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008690571,0.0003081404,0.0001895464,0.0001362384,0.0001693544,0.000003301885,0.0004781565,0.01087991,0.0091578,0.08310211,0.0001792378,0.8953093],"study_design_scores_gemma":[0.00503091,0.00622969,0.000286201,0.00160226,0.0001129711,0.0003032605,0.001616071,0.6728086,0.07387286,0.02085529,0.2156051,0.001676775],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002252991,0.0001335938,0.9887329,0.007502764,0.000143876,0.0008606632,0.00001121911,0.0003287355,0.00003321786],"genre_scores_gemma":[0.9707201,0.0002691929,0.02711931,0.00004313894,0.000008783862,0.0005224281,0.00000316275,0.00001476627,0.001299099],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9684671,"threshold_uncertainty_score":0.3546575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01873880117814438,"score_gpt":0.2621758147648627,"score_spread":0.2434370135867183,"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."}}