{"id":"W1994355768","doi":"10.1115/detc2004-57686","title":"A Multi-Agent Framework for Collaborative Engineering Design and Optimization","year":2004,"lang":"en","type":"article","venue":"","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Interoperation; Collaborative engineering; Computer science; Collaborative design; Concurrent engineering; Multi-agent system; Systems engineering; Software engineering; Intelligent agent; Engineering; Interoperability; Systems design; World Wide Web; Artificial intelligence","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":[0.00006881532,0.00009840936,0.00008636429,0.00005518735,0.00004157628,0.00004629801,0.00003403995,0.00007479197,0.00001226329],"category_scores_gemma":[0.00008645674,0.00009899669,0.00001448397,0.0001814696,0.000007365886,0.00007785428,0.000005647977,0.00005412803,0.000002834381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004155775,"about_ca_system_score_gemma":0.00001404544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.570398e-7,"about_ca_topic_score_gemma":2.364203e-7,"domain_scores_codex":[0.9996156,0.000004122,0.0001052624,0.0001053291,0.00004689102,0.0001228451],"domain_scores_gemma":[0.9997327,0.00007335293,0.00001096095,0.00006518229,0.00005942674,0.00005832181],"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":[0.000002940163,0.0000081253,0.000001652883,0.00001406306,0.00001690418,3.798926e-7,0.0003005362,0.9976572,0.00007501532,0.001681008,0.000009927758,0.000232216],"study_design_scores_gemma":[0.0005520762,0.00002371252,0.000006960419,0.00002500679,0.000008321862,0.000001279561,0.0001082049,0.996103,0.002883091,0.0001171992,0.00004282521,0.0001283514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002381671,0.000245179,0.9985071,0.00006162515,0.0001947022,0.0003259477,0.00000352018,0.000405118,0.00001861887],"genre_scores_gemma":[0.01103483,0.00008949178,0.9886719,0.00003662954,0.00003926366,0.00007295263,0.000006575005,0.00003034454,0.00001801149],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01079667,"threshold_uncertainty_score":0.4036968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02024988065064356,"score_gpt":0.2395279623356951,"score_spread":0.2192780816850516,"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."}}