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A Maximal Entropy Stochastic Process for a Timed Automaton,

  • Nicolas Basset
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7966)

Abstract

Several ways of assigning probabilities to runs of timed automata (TA) have been proposed recently. When only the TA is given, a relevant question is to design a probability distribution which represents in the best possible way the runs of the TA. This question does not seem to have been studied yet. We give an answer to it using a maximal entropy approach. We introduce our variant of stochastic model, the stochastic process over runs which permits to simulate random runs of any given length with a linear number of atomic operations. We adapt the notion of Shannon (continuous) entropy to such processes. Our main contribution is an explicit formula defining a process Y * which maximizes the entropy. This formula is an adaptation of the so-called Shannon-Parry measure to the timed automata setting. The process Y * has the nice property to be ergodic. As a consequence it has the asymptotic equipartition property and thus the random sampling w.r.t. Y * is quasi uniform.

Keywords

Probability Density Function Model Check Maximal Entropy Strong Connectivity Time Automaton 
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

  • Nicolas Basset
    • 1
    • 2
  1. 1.LIGMUniversity Paris-Est Marne-la-Vallée and CNRSFrance
  2. 2.LIAFAUniversity Paris Diderot and CNRSFrance

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