Chapter

Advances in Artificial Intelligence

Volume 4013 of the series Lecture Notes in Computer Science pp 371-382

Trace Equivalence Characterization Through Reinforcement Learning

  • Josée DesharnaisAffiliated withCarnegie Mellon UniversityIFT-GLO, Université Laval
  • , François LavioletteAffiliated withCarnegie Mellon UniversityIFT-GLO, Université Laval
  • , Krishna Priya Darsini MoturuAffiliated withCarnegie Mellon UniversityIFT-GLO, Université Laval
  • , Sami ZhiouaAffiliated withCarnegie Mellon UniversityIFT-GLO, Université Laval

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Abstract

In the context of probabilistic verification, we provide a new notion of trace-equivalence divergence between pairs of Labelled Markov processes. This divergence corresponds to the optimal value of a particular derived Markov Decision Process. It can therefore be estimated by Reinforcement Learning methods. Moreover, we provide some PAC-guarantees on this estimation.