{"id":"W1918192245","doi":"10.1109/ictta.2006.1684903","title":"An Overview of the Analysis and Design of SIGMA: Supervisory IntelliGent Multi-agent system Architecture","year":2006,"lang":"en","type":"article","venue":"","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Unified Modeling Language; Computer science; Systems engineering; Architecture; Software engineering; Multi-agent system; Sequence diagram; Six Sigma; Systems architecture; Sigma; Engineering; Artificial intelligence; Manufacturing engineering; Programming language","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":[0.003193336,0.001142341,0.0008469493,0.002414757,0.0007038339,0.003232448,0.001640166,0.001156216,0.002661274],"category_scores_gemma":[0.004061997,0.0008991667,0.001446835,0.001797885,0.001801547,0.003524939,0.001071753,0.002430164,0.001192039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002038554,"about_ca_system_score_gemma":0.003144444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003653471,"about_ca_topic_score_gemma":0.00265301,"domain_scores_codex":[0.9979253,0.0006575636,0.0002480222,0.00030582,0.0007427667,0.0001204458],"domain_scores_gemma":[0.9983321,0.00061031,0.0001458044,0.0002178169,0.000624834,0.00006918184],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008101726,0.00008276059,0.001655422,0.002162772,0.0001577319,0.0002950933,0.001574349,0.05919535,0.01162109,0.4697326,0.006006947,0.4474349],"study_design_scores_gemma":[0.00004769089,0.0002411409,0.001438476,0.001952849,0.0002055364,0.0007058982,0.000746334,0.2322113,0.02180079,0.3073464,0.433175,0.0001285011],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001469531,0.003645984,0.9884875,0.0004240448,0.00005108796,0.0001544289,0.00005606582,0.0007903906,0.004920765],"genre_scores_gemma":[0.04099422,0.006164799,0.9485324,0.0002100949,0.00005974033,0.0003668131,0.0003167215,0.0002450302,0.003110224],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003653471,"threshold_uncertainty_score":0.01688814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08288491071747374,"score_gpt":0.277566393066454,"score_spread":0.1946814823489803,"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."}}