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arXiv · 2110.00326

Approximate Bisimulation Minimisation

Stefan Kiefer·Qiyi Tang
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Abstract

We propose polynomial-time algorithms to minimise labelled Markov chains whose transition probabilities are not known exactly, have been perturbed, or can only be obtained by sampling. Our algorithms are based on a new notion of an approximate bisimulation quotient, obtained by lumping together states that are exactly bisimilar in a slightly perturbed system. We present experiments that show that our algorithms are able to recover the structure of the bisimulation quotient of the unperturbed system.

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Stefan Kiefer, Qiyi Tang. 2021-10-01. Approximate Bisimulation Minimisation. https://doi.org/10.4230/lipics.fsttcs.2021.28

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