Automated Rare Event Simulation for Stochastic Petri Nets

  • Daniël Reijsbergen
  • Pieter-Tjerk de Boer
  • Werner Scheinhardt
  • Boudewijn Haverkort
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8054)


We introduce an automated approach for applying rare event simulation to stochastic Petri net (SPN) models of highly reliable systems. Rare event simulation can be much faster than standard simulation because it is able to exploit information about the typical behaviour of the system. Previously, such information came from heuristics, human insight, or analysis on the full state space. We present a formal algorithm that obtains the required information from the high-level SPN-description, without generating the full state space. Essentially, our algorithm reduces the state space of the model into a (much smaller) graph in which each node represents a set of states for which the most likely path to failure has the same form. We empirically demonstrate the efficiency of the method with two case studies.


Distance Function Monte Carlo Importance Sampling Tandem Queue Initial Zone 
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 2013

Authors and Affiliations

  • Daniël Reijsbergen
    • 1
  • Pieter-Tjerk de Boer
    • 1
  • Werner Scheinhardt
    • 1
  • Boudewijn Haverkort
    • 1
  1. 1.Center for Telematics & Information TechnologyUniversity of TwenteEnschedeThe Netherlands

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