{"id":"W4393110845","doi":"10.1016/j.arcontrol.2024.100951","title":"Broad-deep network-based fuzzy emotional inference model with personal information for intention understanding in human–robot interaction","year":2024,"lang":"en","type":"article","venue":"Annual Reviews in Control","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Fuzzy inference system; Inference; Artificial intelligence; Computer science; Fuzzy logic; Human–robot interaction; Fuzzy inference; Adaptive neuro fuzzy inference system; Human–computer interaction; Robot; Psychology; Machine learning; Fuzzy control system","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":[0.001178458,0.0001519334,0.0002524947,0.000252643,0.00009625947,0.000232212,0.0002079239,0.00006102346,0.00000829232],"category_scores_gemma":[0.0001586688,0.0001232886,0.00009014869,0.0003907022,0.00002177883,0.001640045,0.00002796014,0.0002797804,0.00001642208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002645162,"about_ca_system_score_gemma":0.00008886423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004625725,"about_ca_topic_score_gemma":0.0001994191,"domain_scores_codex":[0.9987051,0.0001353402,0.0004609388,0.0002471329,0.0001945786,0.0002568658],"domain_scores_gemma":[0.9993765,0.0002441783,0.0001302148,0.0001351459,0.00007318436,0.00004079485],"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":[0.0001018932,0.0000626227,0.001856274,0.000440794,0.00001922208,0.000003382092,0.002489901,0.6462684,0.00001173003,0.1186513,0.001236536,0.228858],"study_design_scores_gemma":[0.0008642568,0.0001439935,0.0004974915,0.00112709,0.000008557089,0.000003411002,0.00006592448,0.9867,6.053983e-7,0.004329982,0.006116812,0.0001418865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007783654,0.001049643,0.9955634,0.001249567,0.0002810359,0.0005235549,0.000005154356,0.00007112473,0.0004781849],"genre_scores_gemma":[0.9904405,0.00008888956,0.008461178,0.0006659651,0.00009312419,0.000153605,0.0000406595,0.000007202786,0.00004881795],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9896622,"threshold_uncertainty_score":0.5027562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04538441744325913,"score_gpt":0.328651947582127,"score_spread":0.2832675301388679,"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."}}