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arXiv 1401.4658cs.LO
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基于可能性测度的计算树逻辑模型检验

Computation Tree Logic Model Checking Based on Possibility Measures

Yongming Li, Yali Li, Zhanyou Ma

摘要:

为了解决可能性理论中不确定信息的系统验证问题,Li和\cite{li12}引入了线性时间特性的模型检验,其中不确定性用可能性测度来建模。Xue,Lei和Li \cite{Xue09}定义了基于可能性测度的计算树逻辑(CTL),称之为可能性CTL(PoCTL)。本文是上述工作的继续。首先,我们研究了PoCTL的表现力。不同于概率CTL,它表明,PoCTL(特别是,定性PoCTL)是更强大的CTL就其表现力。详细给出了基本CTL公式与定性PoCTL公式的等价表达式。给出了一些不能用任何CTL公式表示的PoCTL公式。特别地,用PoCTL公式表示了重复可达性和持久性的一些定性性质.接着,采用CTL模型检测算法,详细讨论了PoCTL模型检测问题的解决方法及其时间复杂度。最后,通过一个实例说明了PoCTL模型检验方法。

英文摘要:

In order to deal with the systematic verification with uncertain infromation in possibility theory, Li and Li \cite{li12} introduced model checking of linear-time properties in which the uncertainty is modeled by possibility measures. Xue, Lei and Li \cite{Xue09} defined computation tree logic (CTL) based on possibility measures, which is called possibilistic CTL (PoCTL). This paper is a continuation of the above work. First, we study the expressiveness of PoCTL. Unlike probabilistic CTL, it is shown that PoCTL (in particular, qualitative PoCTL) is more powerful than CTL with respect to their expressiveness. The equivalent expressions of basic CTL formulae using qualitative PoCTL formulae are presented in detail. Some PoCTL formulae that can not be expressed by any CTL formulae are presented. In particular, some qualitative properties of repeated reachability and persistence are expressed using PoCTL formulae. Next, adapting CTL model-checking algorithm, a method to solve the PoCTL model-checking problem and its time complexity are discussed in detail. Finally, an example is given to illustrate the PoCTL model-checking method.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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