FSTTCS 2006: Foundations of Software Technology and Theoretical Computer Science

Volume 4337 of the series Lecture Notes in Computer Science pp 236-247

Testing Probabilistic Equivalence Through Reinforcement Learning

  • Josée DesharnaisAffiliated withIFT-GLO, Université Laval
  • , François LavioletteAffiliated withIFT-GLO, Université Laval
  • , Sami ZhiouaAffiliated withIFT-GLO, Université Laval

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We propose a new approach to verification of probabilistic processes for which the model may not be available. We use a technique from Reinforcement Learning to approximate how far apart two processes are by solving a Markov Decision Process. If two processes are equivalent, the algorithm will return zero, otherwise it will provide a number and a test that witness the non equivalence. We suggest a new family of equivalences, called K-moment, for which it is possible to do so. The weakest, 1-moment equivalence, is trace-equivalence. The others are weaker than bisimulation but stronger than trace-equivalence.