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.
Work supported by the VKR Center of Excellence MT-LAB and the Sino-Danish Basic Research Center IDEA4CPS.
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References
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Bacci, G., Bacci, G., Guldstrand Larsen, K., Mardare, R. (2013). The BisimDist Library: Efficient Computation of Bisimilarity Distances for Markovian Models. In: Joshi, K., Siegle, M., Stoelinga, M., D’Argenio, P.R. (eds) Quantitative Evaluation of Systems. QEST 2013. Lecture Notes in Computer Science, vol 8054. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40196-1_23
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DOI: https://doi.org/10.1007/978-3-642-40196-1_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40195-4
Online ISBN: 978-3-642-40196-1
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