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Approximate Probabilistic Model Checking

  • Thomas Hérault
  • Richard Lassaigne
  • Frédéric Magniette
  • Sylvain Peyronnet
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2937)

Abstract

Symbolic model checking methods have been extended recently to the verification of probabilistic systems. However, the representation of the transition matrix may be expensive for very large systems and may induce a prohibitive cost for the model checking algorithm. In this paper, we propose an approximation method to verify quantitative properties on discrete Markov chains. We give a randomized algorithm to approximate the probability that a property expressed by some positive LTL formula is satisfied with high confidence by a probabilistic system. Our randomized algorithm requires only a succinct representation of the system and is based on an execution sampling method. We also present an implementation and a few classical examples to demonstrate the effectiveness of our approach.

Keywords

Model Check Execution Path Discrete Time Markov Chain Monotone Formula Symbolic Model Check 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Thomas Hérault
    • 1
  • Richard Lassaigne
    • 2
  • Frédéric Magniette
    • 1
  • Sylvain Peyronnet
    • 1
  1. 1.LRIUniversity Paris XI 
  2. 2.University Paris VII 

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