{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00219508,0.0006063455,0.001184143,0.0006508537,0.001304469,0.002808092,0.002982303,0.001116557,0.02301665],"category_scores_gemma":[0.004541056,0.0009656772,0.0006794857,0.0006251875,0.0007682,0.001780018,0.001718495,0.001872552,0.004880878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000871486,"about_ca_system_score_gemma":0.002747493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004788988,"about_ca_topic_score_gemma":0.006027512,"domain_scores_codex":[0.9991874,0.0001477085,0.00007162926,0.0001864356,0.000298697,0.0001081379],"domain_scores_gemma":[0.9983593,0.0006156258,0.00008823848,0.0003764133,0.0002968805,0.0002635239],"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":[0.004599906,0.001241275,0.007431809,0.001188476,0.000629944,0.000810148,0.001399428,0.2323472,0.05296811,0.1778476,0.0784426,0.4410935],"study_design_scores_gemma":[0.0005687778,0.0001426989,0.0005639308,0.00003246042,0.0001288948,0.0001372335,0.00005803442,0.9058719,0.01533038,0.03016181,0.04693171,0.00007226634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02242925,0.0002695987,0.8854666,0.00035505,0.0006114871,0.0003146572,0.0006088392,0.07886848,0.01107601],"genre_scores_gemma":[0.3215866,0.0002774782,0.6459085,0.0003592967,0.0002675321,0.0005547844,0.001060654,0.006254761,0.0237303],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02301665,"threshold_uncertainty_score":0.07699841,"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."}}