{"id":"W7124307291","doi":"10.65109/qtfy6777","title":"The Importance of Credo in Multiagent Learning","year":2023,"lang":"","type":"article","venue":"","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reinforcement learning; Context (archaeology); Population; Multi-agent system; Social learning","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":[0.006864217,0.0008863047,0.001682446,0.000597593,0.001273891,0.003266806,0.002175943,0.002431233,0.002496253],"category_scores_gemma":[0.03204271,0.0006153143,0.0007192193,0.000659659,0.005102304,0.005600527,0.003261758,0.004262214,0.000281154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001970091,"about_ca_system_score_gemma":0.002219352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002235502,"about_ca_topic_score_gemma":0.001746458,"domain_scores_codex":[0.9953279,0.002842528,0.0001363429,0.0005598046,0.0007418502,0.0003916123],"domain_scores_gemma":[0.9718261,0.02092668,0.001534702,0.002442477,0.001222431,0.002047583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001910825,0.0001313108,0.001805632,0.00009075521,0.00005480309,0.0001444449,0.0001759925,0.6651253,0.0008300783,0.3163149,0.0009892023,0.01414647],"study_design_scores_gemma":[0.00002061925,0.00004808209,0.0001113679,0.00001247374,0.000005792177,0.00002331219,0.00001673805,0.8415381,0.000146269,0.1577035,0.0003603149,0.00001343812],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07565485,0.0007308654,0.9093652,0.003096444,0.0001760473,0.00008331236,0.00008610976,0.0001858295,0.01062146],"genre_scores_gemma":[0.9626589,0.0002118194,0.03516622,0.0002568466,0.00009506398,0.00008685326,0.00002747573,0.00003250828,0.001464284],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006864217,"threshold_uncertainty_score":0.03630191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03153244086601935,"score_gpt":0.2863773328859435,"score_spread":0.2548448920199242,"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."}}