{"id":"W1783308240","doi":"10.1007/978-3-540-74573-0_26","title":"A Probabilistic Multi-agent Scheduler Implemented in JXTA","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Acadia University","funders":"","keywords":"Probabilistic logic; Computer science; Scheduling (production processes); Distributed computing; Schedule; Interval (graph theory); Multi-agent system; Constraint satisfaction problem; Graph; Theoretical computer science; Mathematical optimization; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001110737,0.0004408088,0.0004019348,0.001390605,0.0001471769,0.0003534723,0.001581227,0.0002653168,0.0001077196],"category_scores_gemma":[0.0001387942,0.0004281527,0.0000960744,0.0009203295,0.0004237041,0.0004754977,0.0007958342,0.0007211789,0.00005045583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006213083,"about_ca_system_score_gemma":0.0005972299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004531019,"about_ca_topic_score_gemma":0.001394813,"domain_scores_codex":[0.9964564,0.00003628678,0.0006946699,0.00136052,0.0007800547,0.0006720419],"domain_scores_gemma":[0.9981515,0.0002540864,0.000248934,0.0009480455,0.0002252194,0.0001721464],"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":[0.000006282248,0.0000678068,0.000654489,0.00003883336,0.000008407655,0.0001118251,0.0007114182,0.09831145,0.00004046181,0.0307342,0.00000840148,0.8693064],"study_design_scores_gemma":[0.000672245,0.00007902477,0.002320808,0.0002729459,0.000004563804,0.00005388245,3.493356e-7,0.9778574,0.0001314566,0.01707569,0.0009289226,0.0006026852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00009447547,0.00009803878,0.9954711,0.0004850115,0.001209848,0.0006746104,0.000004262361,0.000142572,0.001820099],"genre_scores_gemma":[0.1220191,0.0000330426,0.8759417,0.001503542,0.0001597294,0.00001735714,0.00001195111,0.00003277338,0.0002808192],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.879546,"threshold_uncertainty_score":0.999817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0396655249298687,"score_gpt":0.292312850167585,"score_spread":0.2526473252377163,"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."}}