On Incremental Quantitative Verification for Probabilistic Systems

HOWARD-60 Pub Date : 2011-12-20 DOI:10.29007/bmcf
M. Kwiatkowska, D. Parker, Hongyang Qu, M. Ujma
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引用次数: 7

Abstract

Quantitative verification techniques offer an effective means of computing performance and reliability properties for a wide range of systems. In many cases, it is necessary to perform repeated analyses of a system, for example to identify trends in results, determine optimal system parameters or when performing online analysis for adaptive systems. We argue the need for incremental quantitative verification techniques which are able to re-use results from previous verification runs in order to improve efficiency. We report on recently proposed techniques for incremental quantitative verification of Markov decision processes, based on a decomposition of the model into its strongly connected components. We give an overview of the method, describe a number of useful optimisations and show experimental results that illustrate significant gains in run-time performance using the incremental approach.
关于概率系统的增量定量验证
定量验证技术为广泛的系统提供了计算性能和可靠性特性的有效手段。在许多情况下,有必要对系统进行重复分析,例如确定结果的趋势,确定最佳系统参数或对自适应系统进行在线分析。我们认为需要增量的定量验证技术,它能够重用以前的验证运行的结果,以提高效率。我们报告了最近提出的基于将模型分解为其强连接组件的马尔可夫决策过程的增量定量验证技术。我们概述了该方法,描述了一些有用的优化,并展示了实验结果,说明使用增量方法在运行时性能方面的显着收益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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