{"id":"W2003520601","doi":"10.1016/j.ic.2013.02.002","title":"Testing probabilistic equivalence through Reinforcement Learning","year":2013,"lang":"en","type":"article","venue":"Information and Computation","topic":"Formal Methods in Verification","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"","keywords":"Equivalence (formal languages); Mathematics; Bisimulation; Decidability; Probabilistic logic; Discrete mathematics; Theoretical computer science; Computer science","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":[0.01190354,0.001369407,0.001884951,0.001401866,0.0009380445,0.002100164,0.003491216,0.002143582,0.005523534],"category_scores_gemma":[0.1386798,0.000871078,0.001797019,0.0006763618,0.006685519,0.008585217,0.005516795,0.004063528,0.0004627171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002452075,"about_ca_system_score_gemma":0.004483331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00327405,"about_ca_topic_score_gemma":0.003070675,"domain_scores_codex":[0.9768014,0.01194433,0.0008989738,0.003579889,0.005215216,0.00156028],"domain_scores_gemma":[0.8123876,0.1655761,0.005139655,0.0110128,0.004238836,0.001644991],"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":[0.001955469,0.001265062,0.0118309,0.0004385509,0.0003142233,0.000396571,0.0004677142,0.5438135,0.005027236,0.2926195,0.00224264,0.1396286],"study_design_scores_gemma":[0.0001344588,0.0001089594,0.0003609817,0.00002432903,0.00002308709,0.00003240449,0.0000374629,0.7592206,0.002808762,0.2369922,0.0002388429,0.00001795091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1291175,0.00008604939,0.8649798,0.001043285,0.00008765264,0.0001577597,0.0001129482,0.00112417,0.00329084],"genre_scores_gemma":[0.9299728,0.00003165222,0.06869581,0.0001654408,0.00003408656,0.0001188112,0.0001585758,0.0001251376,0.000697908],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01190354,"threshold_uncertainty_score":0.0629527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05048742108083468,"score_gpt":0.2990773554567007,"score_spread":0.248589934375866,"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."}}