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Property-driven State-Space Coarsening for Continuous Time Markov Chains

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publication Quantitative Evaluation of Systems
Subtitle of host publication13th International Conference on Quantitative Evaluation of SysTems (QEST 2016)
PublisherSpringer International Publishing
Number of pages16
ISBN (Electronic)978-3-319-43425-4
ISBN (Print)978-3-319-43424-7
StatePublished - 3 Aug 2016

Publication series

NameLecture Notes in Computer Science (LNCS)
PublisherSpringer International Publishing
ISSN (Print)0302-9743


Dynamical systems with large state-spaces are often expensive to thoroughly explore experimentally. Coarse-graining methods aim to define simpler systems which are more amenable to analysis and exploration; most current methods, however, focus on a priori state aggregation based on similarities in transition rates, which is not necessarily reflected in similar behaviours at the level of trajectories. We propose a way to coarsen the state-space of a system which optimally preserves the satisfaction of a set of logical specifications about the system's trajectories. Our approach is based on Gaussian Process emulation and Multi-Dimensional Scaling, a dimensionality reduction technique which optimally preserves distances in non-Euclidean spaces. We show how to obtain low-dimensional visualisations of the system's state-space from the perspective of properties' satisfaction, and how to define macro-states which behave coherently with respect to the specifications. Our approach is illustrated on a non-trivial running example, showing promising performance and high computational efficiency.

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