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Faster Statistical Model Checking for Unbounded Temporal Properties

  • Przemysław Daca
  • Thomas A. Henzinger
  • Jan Křetínský
  • Tatjana Petrov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9636)

Abstract

We present a new algorithm for the statistical model checking of Markov chains with respect to unbounded temporal properties, including full linear temporal logic. The main idea is that we monitor each simulation run on the fly, in order to detect quickly if a bottom strongly connected component is entered with high probability, in which case the simulation run can be terminated early. As a result, our simulation runs are often much shorter than required by termination bounds that are computed a priori for a desired level of confidence on a large state space. In comparison to previous algorithms for statistical model checking our method is not only faster in many cases but also requires less information about the system, namely, only the minimum transition probability that occurs in the Markov chain. In addition, our method can be generalised to unbounded quantitative properties such as mean-payoff bounds.

Keywords

Markov Chain Goal State Linear Temporal Logic Confidence Bound Reachability Analysis 
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 2016

Authors and Affiliations

  • Przemysław Daca
    • 1
  • Thomas A. Henzinger
    • 1
  • Jan Křetínský
    • 2
  • Tatjana Petrov
    • 1
  1. 1.IST AustriaKlosterneuburgAustria
  2. 2.Institut Für InformatikTechnische Universität MünchenMunichGermany

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