{"id":"W3040611580","doi":"10.1007/978-3-030-52152-3_22","title":"Learning to Model Another Agent’s Beliefs: A Preliminary Approach","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"British Columbia Institute of Technology","funders":"","keywords":"Computer science; Process (computing); Order (exchange); Artificial intelligence; Intelligent agent; Belief revision; Natural (archaeology); Cognitive science; Psychology","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":[0.002680745,0.001030606,0.0008882665,0.0006604135,0.000724939,0.002571186,0.003286838,0.00185031,0.008614053],"category_scores_gemma":[0.008707054,0.0005779132,0.001508023,0.001002536,0.00232854,0.006873598,0.002792889,0.003398416,0.001132595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00151092,"about_ca_system_score_gemma":0.001399628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003542554,"about_ca_topic_score_gemma":0.003427872,"domain_scores_codex":[0.998825,0.0005319915,0.00007507188,0.0002870697,0.0002126816,0.00006821613],"domain_scores_gemma":[0.9946583,0.003927324,0.0001611118,0.0006006576,0.0004517802,0.0002008441],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001124565,0.0002170566,0.0009328548,0.0002390275,0.0001231319,0.0001912462,0.0006917423,0.08215757,0.001441247,0.8354437,0.004057163,0.07439279],"study_design_scores_gemma":[0.00001825287,0.0001034994,0.0002189277,0.00004354202,0.00003324344,0.00008205725,0.00009140425,0.244487,0.0007245319,0.7485799,0.005592836,0.00002479998],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005477147,0.0003449718,0.9817822,0.001746731,0.00005119482,0.00006837269,0.0001217616,0.0001208559,0.01028687],"genre_scores_gemma":[0.3823143,0.001383653,0.5924107,0.0009153389,0.0003568654,0.0004909817,0.0006272909,0.0001014053,0.02139937],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008614053,"threshold_uncertainty_score":0.02881694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02730317927246811,"score_gpt":0.2364933274448766,"score_spread":0.2091901481724085,"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."}}