{"id":"W4391093351","doi":"10.1109/bigdata59044.2023.10386490","title":"GPT-in-the-Loop: Supporting Adaptation in Multiagent Systems","year":2023,"lang":"en","type":"article","venue":"","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adaptation (eye); Computer science; Loop (graph theory); Distributed computing; Neuroscience; Mathematics; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001267361,0.00007360252,0.00008846403,0.000200667,0.00004508359,0.0001686009,0.000569493,0.00003216055,0.000005476654],"category_scores_gemma":[0.0001173982,0.00005703758,0.00002239109,0.0009203041,0.000008960512,0.0002575418,0.0001123514,0.000122617,0.0003181505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004635803,"about_ca_system_score_gemma":0.00003148599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002903806,"about_ca_topic_score_gemma":0.00005587246,"domain_scores_codex":[0.9987181,0.0001055254,0.0003476113,0.0001963392,0.00033693,0.0002955225],"domain_scores_gemma":[0.9993492,0.0002017523,0.00008967058,0.0003127798,0.00002298592,0.00002362413],"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":[3.701869e-7,0.000006334235,0.003058763,0.00001324819,0.000001379404,0.00004241185,0.003041038,0.9812924,0.0000411334,0.0105985,0.0005082113,0.001396233],"study_design_scores_gemma":[0.0001627545,0.00002170736,0.006838765,0.0000219973,6.372909e-7,0.00000324962,0.001509012,0.9906808,0.0000340043,0.0000335538,0.0006210956,0.00007239714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03243621,0.00001183152,0.9608605,0.000705797,0.0004577094,0.0003318139,9.040608e-8,0.0002242225,0.004971839],"genre_scores_gemma":[0.993718,0.000006133823,0.00369681,0.0001312199,0.00002262649,0.00002663446,0.000004159655,0.000005219923,0.00238918],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9612818,"threshold_uncertainty_score":0.4089287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05118880440808801,"score_gpt":0.2954463019389528,"score_spread":0.2442574975308648,"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."}}