Compositional Stochastic Model Checking Probabilistic Automata via Assume-guarantee Reasoning

DOI

10.2991/ijndc.k.190918.001How to use a DOI?

Keywords

Stochastic model checking; assume-guarantee reasoning; symmetric assume-guarantee rule; learning algorithm; probabilistic automata

Abstract

Stochastic model checking is the extension and generalization of the classical model checking. Compared with classical model checking, stochastic model checking faces more severe state explosion problem, because it combines classical model checking algorithms and numerical methods for calculating probabilities. For dealing with this, we first apply symmetric assume-guarantee rule symmetric (SYM) for two-component systems and symmetric assume-guarantee rule for n-component systems into stochastic model checking in this paper, and propose a compositional stochastic model checking framework of probabilistic automata based on the NL^* algorithm. It optimizes the existed compositional stochastic model checking process to draw a conclusion quickly, in cases the system model does not satisfy the quantitative properties. We implement the framework based on the PRISM tool, and several large cases are used to demonstrate the performance of it.

Copyright

© 2019 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Yang Liu
AU  - Rui Li
PY  - 2020
DA  - 2020/04/09
TI  - Compositional Stochastic Model Checking Probabilistic Automata via Assume-guarantee Reasoning
JO  - International Journal of Networked and Distributed Computing
SP  - 94
EP  - 107
VL  - 8
IS  - 2
SN  - 2211-7946
UR  - https://doi.org/10.2991/ijndc.k.190918.001
DO  - 10.2991/ijndc.k.190918.001
ID  - Liu2020
ER  -