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Model Checking of Continuous-Time Markov Chains Against Timed Automata Specifications

机译:定时自动机指标对连续时间马尔可夫链的模型检验

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We study the verification of a finite continuous-time Markov chain (CTMC) Cagainst a linear real-time specification given as a deterministic timedautomaton (DTA) A with finite or Muller acceptance conditions. The centralquestion that we address is: what is the probability of the set of paths of Cthat are accepted by A, i.e., the likelihood that C satisfies A? It is shownthat under finite acceptance criteria this equals the reachability probabilityin a finite piecewise deterministic Markov process (PDP), whereas for Mulleracceptance criteria it coincides with the reachability probability of terminalstrongly connected components in such a PDP. Qualitative verification is shownto amount to a graph analysis of the PDP. Reachability probabilities in ourPDPs are then characterized as the least solution of a system of Volterraintegral equations of the second type and are shown to be approximated by thesolution of a system of partial differential equations. For single-clock DTA,this integral equation system can be transformed into a system of linearequations where the coefficients are solutions of ordinary differentialequations. As the coefficients are in fact transient probabilities in CTMCs,this result implies that standard algorithms for CTMC analysis suffice toverify single-clock DTA specifications.
机译:我们研究了一个有限的连续时间马尔可夫链(CTMC)的验证,这是一个线性实时规范,该规范以具有确定或穆勒接受条件的确定性定时自动机(DTA)A给出。我们要解决的中心问题是:A接受C的路径集的概率是多少,即C满足A的可能性是多少?结果表明,在有限接受标准下,这等于有限分段确定性马尔可夫过程(PDP)中的可达性概率,而对于Mulleracceptance标准,它与此类PDP中的终端强连接组件的可达性概率相符。显示的定性验证相当于对PDP的图形分析。然后,我们的PDP中的可到达性概率被表征为第二类型Volterraintegral方程组的最小解,并显示为通过偏微分方程组的解来近似。对于单时钟DTA,可以将该积分方程组转换为线性方程组,其中系数是普通微分方程的解。由于系数实际上是CTMC中的瞬态概率,因此该结果表明CTMC分析的标准算法足以验证单时钟DTA规范。

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