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Time-Sensitive Importance Splitting
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AI
DOI：10.1007/978-3-032-05792-1_2.png)
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Abstract
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State-of-the-art methods for rare event simulation of non-Markovian models face practical or theoretical limits if observing the event of interest requires prior knowledge or information on the timed behavior of the system. In this paper, we attack both limits by extending importance splitting with a time-sensitive importance function. To this end, we perform backwards reachability search from the target states, considering information about the lower and upper bounds of the active timers in order to steer the generation of paths towards the rare event. We have developed a prototype implementation of the approach for input/output stochastic automata within the MODEST TOOLSET. Preliminary experiments show the potential of the approach in estimating rare event probabilities for an example from reliability engineering.
Keywords:
STOCHASTIC PETRI NETS
NON-MARKOVIAN MODELS
TRANSIENT ANALYSIS
SYSTEMS
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