{"id":"W2039519793","doi":"10.1109/wi-iat.2012.33","title":"A Hybrid Cooperative Behavior Learning Method for a Rule-Based Shout-Ahead Architecture","year":2012,"lang":"en","type":"article","venue":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Reinforcement learning; Computer science; Architecture; Task (project management); Set (abstract data type); Artificial intelligence; Quality (philosophy); Hybrid learning; Machine learning; Evolutionary computation; Engineering","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":[0.001168906,0.000560108,0.0005381012,0.000534803,0.0004848181,0.0006483287,0.001999511,0.001003612,0.003518891],"category_scores_gemma":[0.002158558,0.0004098528,0.0005590178,0.0003601204,0.000888022,0.001036111,0.001066639,0.001107006,0.0006765001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005735062,"about_ca_system_score_gemma":0.0009195405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003427734,"about_ca_topic_score_gemma":0.00403347,"domain_scores_codex":[0.9993988,0.0001651474,0.00003138677,0.000130009,0.0002248694,0.00004981413],"domain_scores_gemma":[0.9991928,0.0003200851,0.00006134082,0.0001386206,0.0002229875,0.00006419139],"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":[0.0001362133,0.0003139165,0.001556521,0.0001128501,0.0001357904,0.0002222232,0.0004101923,0.5903579,0.01685193,0.04194896,0.001878389,0.3460751],"study_design_scores_gemma":[0.00001069856,0.0000327244,0.00006539773,0.000004499779,0.000007725408,0.00002023934,0.000009075337,0.9942063,0.001203452,0.003679528,0.0007538857,0.000006365993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007980084,0.00003891762,0.9898107,0.00006666423,0.00001591078,0.00005684854,0.000008364083,0.0003879787,0.001634498],"genre_scores_gemma":[0.3402515,0.00007430127,0.6520872,0.0001397135,0.00002357459,0.0003482456,0.00006051272,0.0001015588,0.006913285],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003518891,"threshold_uncertainty_score":0.0117718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05280796709589249,"score_gpt":0.3403408594984353,"score_spread":0.2875328924025429,"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."}}