{"id":"W4205991048","doi":"10.1109/lra.2021.3135940","title":"Learning an Explainable Trajectory Generator Using the Automaton Generative Network (AGN)","year":2021,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Toyota Research Institute","keywords":"Computer science; Artificial intelligence; Component (thermodynamics); Leverage (statistics); Automaton; Theoretical computer science; Generator (circuit theory); Pipeline (software); Machine learning; Key (lock); Representation (politics); Finite-state machine; Generative model; Artificial neural network; Generative grammar; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004756634,0.0005117052,0.0003297576,0.0003909238,0.0002499955,0.0004651213,0.001111776,0.0008216505,0.002445555],"category_scores_gemma":[0.002268171,0.0003924716,0.0006710821,0.0003296728,0.0006697145,0.0009529478,0.0008041974,0.001209053,0.0003984565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006965037,"about_ca_system_score_gemma":0.0006563919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003954107,"about_ca_topic_score_gemma":0.007228166,"domain_scores_codex":[0.9998245,0.00004567434,0.000007420733,0.00006797913,0.0000343019,0.00002015935],"domain_scores_gemma":[0.9992762,0.0004646735,0.00006576426,0.00009496979,0.00006521093,0.00003317635],"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":[0.00004005727,0.00002498583,0.001063648,0.00003731249,0.00002727607,0.00009822997,0.00005004132,0.9367113,0.002132705,0.01939074,0.0009059524,0.03951774],"study_design_scores_gemma":[0.000002241118,0.000006673198,0.00004301683,0.000002101859,0.000002499733,0.00001079215,0.000001859471,0.9930961,0.0003104856,0.006298328,0.0002242884,0.000001734774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02786665,0.00007450943,0.968803,0.000302489,0.00002530486,0.00003466805,0.0001759892,0.00112403,0.00159328],"genre_scores_gemma":[0.7640328,0.0001725983,0.230536,0.0002025438,0.00003891493,0.0001883048,0.0006512922,0.0002047541,0.003972857],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003954107,"threshold_uncertainty_score":0.008181155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01624430306944624,"score_gpt":0.2481310575836374,"score_spread":0.2318867545141912,"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."}}