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Importance Sampling for Stochastic Timed Automata

  • Cyrille Jegourel
  • Kim G. Larsen
  • Axel Legay
  • Marius Mikučionis
  • Danny Bøgsted Poulsen
  • Sean Sedwards
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9984)

Abstract

We present an importance sampling framework that combines symbolic analysis and simulation to estimate the probability of rare reachability properties in stochastic timed automata. By means of symbolic exploration, our framework first identifies states that cannot reach the goal. A state-wise change of measure is then applied on-the-fly during simulations, ensuring that dead ends are never reached. The change of measure is guaranteed by construction to reduce the variance of the estimator with respect to crude Monte Carlo, while experimental results demonstrate that we can achieve substantial computational gains.

Notes

Acknowledgements

This research has received funding from the Sino-Danish Basic Research Centre, IDEA4CPS, funded by the Danish National Research Foundation and the National Science Foundation, China, the Innovation Fund Denmark centre DiCyPS, as well as the ERC Advanced Grant LASSO. Other funding has been provided by the Self Energy-Supporting Autonomous Computation (SENSATION) and Collective Adaptive Systems Synthesis with Non-zero-sum Games (CASSTING) European FP7-ICT projects, and the Embedded Multi-Core systems for Mixed Criticality (EMC2) ARTEMIS project.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Cyrille Jegourel
    • 1
  • Kim G. Larsen
    • 2
  • Axel Legay
    • 2
    • 3
  • Marius Mikučionis
    • 2
  • Danny Bøgsted Poulsen
    • 2
  • Sean Sedwards
    • 3
  1. 1.National University of SingaporeSingaporeSingapore
  2. 2.Department of Computer ScienceAalborg UniversityAalborgDenmark
  3. 3.Inria Rennes – Bretagne AtlantiqueRennesFrance

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