{"id":"W2405454180","doi":"","title":"Solving Distributed Constraint Optimization Problems - An Evolutionary Approach.","year":2011,"lang":"en","type":"article","venue":"International Conference on Agents and Artificial Intelligence","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Mathematical optimization; Constraint (computer-aided design); Evolutionary computation; Constrained 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001111315,0.0001589896,0.0001080072,0.0001093951,0.0001039592,0.0001226081,0.0002109714,0.00007685256,0.001099017],"category_scores_gemma":[0.00003347212,0.0001581449,0.00002948047,0.0001060616,0.0001124127,0.0002678627,0.00002809645,0.0001462721,0.00002893583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004173291,"about_ca_system_score_gemma":0.00002079621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002313309,"about_ca_topic_score_gemma":0.000002801596,"domain_scores_codex":[0.9990334,0.00002450185,0.0003055414,0.0002594937,0.0002033213,0.0001737759],"domain_scores_gemma":[0.9995207,0.00001448307,0.0000522865,0.0001230593,0.0001729952,0.0001164877],"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":[0.00002200113,0.0001820437,0.0001248893,0.00001044865,0.00004077613,0.00000435703,0.0009961611,0.8525014,0.00008947331,0.1232676,0.00003290919,0.022728],"study_design_scores_gemma":[0.00003365014,0.00005055939,0.0001766811,0.00003428795,0.000006289721,0.000007908548,0.0008624058,0.993439,0.0005818207,0.004579866,0.00004083098,0.0001867043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00529635,0.00001990086,0.9748467,0.00007408587,0.0005816444,0.0001518432,0.00006689505,0.0002271604,0.01873548],"genre_scores_gemma":[0.904075,0.0001254847,0.09537887,0.00006185218,0.00008251959,0.00002300292,0.0002055339,0.00001643497,0.00003133001],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8987786,"threshold_uncertainty_score":0.9998141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1286852578923441,"score_gpt":0.2848074341875055,"score_spread":0.1561221762951613,"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."}}