{"id":"W4375798913","doi":"10.1109/lra.2023.3273421","title":"Multi-Abstractive Neural Controller: An Efficient Hierarchical Control Architecture for Interactive Driving","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Toyota Research Institute","keywords":"Interpretability; Controller (irrigation); Computer science; Artificial neural network; Artificial intelligence; Set (abstract data type); Machine learning; Programming language","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":[0.0003422221,0.0002154545,0.0002588879,0.0002594875,0.0002905733,0.0003690407,0.0003615557,0.00007182764,0.000001010397],"category_scores_gemma":[0.0001439061,0.0001973071,0.00009598145,0.0002418092,0.00008042701,0.0004206827,0.00006243196,0.000294901,0.00001286038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005531248,"about_ca_system_score_gemma":0.00002311267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004064932,"about_ca_topic_score_gemma":0.000001789214,"domain_scores_codex":[0.9983943,0.0001276484,0.0003398891,0.0004335137,0.0002903978,0.0004142804],"domain_scores_gemma":[0.998525,0.0007811995,0.000215944,0.0002520414,0.00008608885,0.0001397151],"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":[0.00001707516,0.00002781019,0.00009888705,0.0000154112,0.00004410351,0.000006956865,0.001116481,0.9843815,0.01021241,0.0008695204,0.000125368,0.003084494],"study_design_scores_gemma":[0.001775135,0.0001282805,0.00950422,0.00002793756,0.00001854162,0.000009747995,0.00004174648,0.9879948,0.0001380381,0.00007026967,0.00007178148,0.0002195283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06901067,0.000003434479,0.921612,0.007518813,0.0007980746,0.0006032728,0.000005365678,0.0004418354,0.000006554151],"genre_scores_gemma":[0.9280555,0.000001403472,0.07024477,0.001448839,0.0001435343,0.00004605089,0.00001381446,0.00002058715,0.00002552631],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8590448,"threshold_uncertainty_score":0.8045951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0146286324787583,"score_gpt":0.2679701900467087,"score_spread":0.2533415575679504,"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."}}