Testing Probabilistic Equivalence Through Reinforcement Learning

  • Josée Desharnais
  • François Laviolette
  • Sami Zhioua
Conference paper

DOI: 10.1007/11944836_23

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4337)
Cite this paper as:
Desharnais J., Laviolette F., Zhioua S. (2006) Testing Probabilistic Equivalence Through Reinforcement Learning. In: Arun-Kumar S., Garg N. (eds) FSTTCS 2006: Foundations of Software Technology and Theoretical Computer Science. FSTTCS 2006. Lecture Notes in Computer Science, vol 4337. Springer, Berlin, Heidelberg

Abstract

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Josée Desharnais
    • 1
  • François Laviolette
    • 1
  • Sami Zhioua
    • 1
  1. 1.IFT-GLOUniversité LavalQuébecCanada

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