{"id":"W2682984199","doi":"10.1299/jsmermd.2013._1p1-b03_1","title":"1P1-B03 Adaptive Behavior Generation using Attractor Dynamics of Recurrent Neural Network(Neurorobotics & Cognitive Robotics)","year":2013,"lang":"en","type":"article","venue":"The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Artificial intelligence; Variance (accounting); Computer science; Gradient descent; Object (grammar); Artificial neural network; Humanoid robot; Attractor; Robotics; Function (biology); Robot; Machine learning; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003156732,0.0003966155,0.0005064968,0.0001151574,0.0003241431,0.0002688333,0.0008369079,0.0001632146,0.000009837778],"category_scores_gemma":[0.00004195834,0.0003187444,0.0001340715,0.0004758725,0.0002528698,0.0006557914,0.0004708098,0.0005236561,0.000006713481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006814732,"about_ca_system_score_gemma":0.0001089865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005260127,"about_ca_topic_score_gemma":0.00003031662,"domain_scores_codex":[0.9976179,0.00003731025,0.0006783773,0.0005937457,0.0004766151,0.0005960696],"domain_scores_gemma":[0.9969463,0.0001485809,0.0007380928,0.0003017644,0.001671385,0.0001938671],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002399553,0.000265406,0.0002710304,0.00003810057,0.00005424174,4.807661e-7,0.0005800357,0.1969136,0.00321414,0.790433,0.000232965,0.007972898],"study_design_scores_gemma":[0.0003460753,0.0005950106,0.000450407,0.0001354944,0.0001464314,0.00001697137,0.0005373193,0.9865416,0.0004103564,0.01044674,0.000009449819,0.000364153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.56154,0.0002756455,0.4307122,0.00411526,0.0006665168,0.002211677,0.00008549508,0.0001140455,0.0002791522],"genre_scores_gemma":[0.9559032,0.0002865551,0.04331696,0.0001386985,0.0001586963,0.00005404864,0.00002500658,0.00003239608,0.00008443259],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.789628,"threshold_uncertainty_score":0.9999264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06615076980717809,"score_gpt":0.2804627628034467,"score_spread":0.2143119929962686,"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."}}