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The BisimDist Library: Efficient Computation of Bisimilarity Distances for Markovian Models

  • Giorgio Bacci
  • Giovanni Bacci
  • Kim Guldstrand Larsen
  • Radu Mardare
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8054)

Abstract

This paper presents a library for exactly computing the bisimilarity Kantorovich-based pseudometrics between Markov chains and between Markov decision processes. These are distances that measure the behavioral discrepancies between non-bisimilar systems. They are computed by using an on-the-fly greedy strategy that prevents the exhaustive state space exploration and does not require a complete storage of the data structures. Tests performed on a consistent set of (pseudo)randomly generated instances show that our algorithm improves the efficiency of the previously proposed iterative algorithms, on average, with orders of magnitude. The tool is available as a Mathematica package library.

Keywords

Discount Factor Markov Decision Process Probability Transition Matrix Greedy Strategy Action Label 
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

  • Giorgio Bacci
    • 1
  • Giovanni Bacci
    • 1
  • Kim Guldstrand Larsen
    • 1
  • Radu Mardare
    • 1
  1. 1.Department of Computer ScienceAalborg UniversityDenmark

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