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Computation tree logic model checking based on possibility measures

机译:基于可能性测度的计算树逻辑模型检查

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In order to deal with the systematic verification with uncertain information in possibility theory, Li and Li (2013) introduced model checking of linear-time properties in which the uncertainty is modeled by possibility measures. Xue, Lei and Li (2011) 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 cannot 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.
机译:为了处理可能性理论中不确定信息的系统验证,Li和Li(2013)引入了线性时间属性的模型检验,其中不确定性通过可能性度量建模。 Xue,Lei和Li(2011)基于可能性度量定义了计算树逻辑(CTL),称为可能性CTL(PoCTL)。本文是以上工作的延续。首先,我们研究PoCTL的表达能力。与概率CTL不同,它显示PoCTL(特别是定性PoCTL)在表达能力方面比CTL更强大。详细介绍了使用定性PoCTL公式的基本CTL公式的等效表达式。提出了一些不能由任何CTL公式表示的PoCTL公式。特别是,使用PoCTL公式表示了重复可达性和持久性的一些定性性质。接下来,结合CTL模型检查算法,详细讨论了一种解决PoCTL模型检查问题的方法及其时间复杂度。最后,给出一个例子来说明PoCTL模型检查方法。

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