{"id":"W4283794194","doi":"10.1609/aaai.v36i5.20524","title":"Using Conditional Independence for Belief Revision","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Belief revision; Dependency (UML); Independence (probability theory); Conditional independence; Computer science; Domain (mathematical analysis); ENCODE; Operator (biology); Knowledge base; Belief structure; Artificial intelligence; Theoretical computer science; Mathematics","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":[0.0009421736,0.0001950868,0.0002411297,0.000132919,0.0008209592,0.0001930552,0.002390874,0.00006452129,0.0001722961],"category_scores_gemma":[0.0004586553,0.0001577007,0.000166063,0.000621042,0.0001807462,0.0003958548,0.0009280055,0.0003625911,0.00002300558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001132004,"about_ca_system_score_gemma":0.0002330209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002093519,"about_ca_topic_score_gemma":0.000002609509,"domain_scores_codex":[0.9978724,0.00002853328,0.0004663477,0.0005465723,0.0007339182,0.0003522766],"domain_scores_gemma":[0.9982991,0.0001487153,0.0004412875,0.0002677632,0.0007637439,0.00007936427],"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":[0.00004375691,0.0001382729,0.000110894,0.00002883685,0.000009102943,3.85014e-7,0.0007989715,0.0002917329,0.01750018,0.964824,0.0003339241,0.01591998],"study_design_scores_gemma":[0.00005911311,0.0004238294,0.00008620892,0.00009864455,0.00001617835,0.00002542948,0.0006156476,0.2314889,0.1773257,0.5880101,0.001544503,0.0003057377],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1626853,0.0002172821,0.8025934,0.00453555,0.002708455,0.002421633,0.00008885423,0.0003091766,0.02444029],"genre_scores_gemma":[0.9912483,0.00001101856,0.008017979,0.0002755988,0.00009587544,0.00005945162,0.000001774082,0.00001126212,0.0002787577],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8285629,"threshold_uncertainty_score":0.643085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1380607233127197,"score_gpt":0.3326342931215088,"score_spread":0.1945735698087891,"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."}}