{"id":"W4323266611","doi":"10.1016/j.artint.2023.103897","title":"Temporal logic explanations for dynamic decision systems using anchors and Monte Carlo Tree Search","year":2023,"lang":"en","type":"article","venue":"Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Black box; Monte Carlo tree search; Artificial intelligence; Decision tree; Machine learning; Monte Carlo method; State (computer science); Artificial neural network; Tree (set theory); Perception; Control (management); Complex system; Theoretical computer science; Algorithm; 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":[0.0008950437,0.0001569449,0.0001962059,0.0003178343,0.0004712577,0.0003107851,0.0006021157,0.00008674119,0.000003049333],"category_scores_gemma":[0.0004257413,0.0001523729,0.00005768744,0.000972202,0.00008841167,0.0004710603,0.0003081766,0.0001826261,0.00004194013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000877166,"about_ca_system_score_gemma":0.00006977822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004370731,"about_ca_topic_score_gemma":0.0002156168,"domain_scores_codex":[0.998249,0.0001030969,0.0003921762,0.0005219593,0.0003305674,0.000403235],"domain_scores_gemma":[0.9985428,0.0007271142,0.00009378903,0.0003781257,0.0001565945,0.0001016176],"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":[0.0000201813,0.0000181282,0.0004601397,0.00002404,0.00001138128,0.00002081124,0.001071651,0.7909108,0.0007244222,0.06254537,0.00004308786,0.14415],"study_design_scores_gemma":[0.00002969996,0.0000604639,0.0002679749,0.00004573102,0.00000623737,0.000009772506,0.0008555433,0.9840059,0.0002171584,0.01421318,0.0001202477,0.0001680403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.157808,0.0001062137,0.8403348,0.0003195982,0.0008288312,0.0003534326,0.000006294266,0.0002132285,0.00002965845],"genre_scores_gemma":[0.9357902,0.000022433,0.06395075,0.00002154195,0.00009327898,0.00002824064,0.000004727789,0.00001660781,0.00007216002],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7779822,"threshold_uncertainty_score":0.6213588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1235831946043025,"score_gpt":0.3831200242106715,"score_spread":0.259536829606369,"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."}}