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Quantitative Timed Analysis of Interactive Markov Chains

机译:互动马尔可夫链的定量定时分析

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This paper presents new algorithms and accompanying tool support for analyzing interactive Markov chains (IMCs), a stochastic timed 1(1/2)-player game in which delays are exponentially distributed. IMCs are compositional and act as semantic model for engineering formalisms such as AADL and dynamic fault trees. We provide algorithms for determining the extremal expected time of reaching a set of states, and the long-run average of time spent in a set of states. The prototypical tool IMCA supports these algorithms as well as the synthesis of ε-optimal piecewise constant timed policies for timed reachability objectives. Two case studies show the feasibility and scalability of the algorithms.
机译:本文介绍了用于分析交互式马尔可夫链(IMC)的新算法和随附的工具支持,该算法是一种随机定时1(1/2)--Player游戏,其中延迟是指数分布的。 IMCs是组成的,充当工程形式主义等语义模型,如Aadl和动态故障树。我们提供确定达到一组状态的极值预期时间的算法,以及在一组状态下花费的长期时间。原型工具IMCA支持这些算法以及ε-最佳分段恒定定时策略的合成,以进行定时可达性目标。两种案例研究表明了算法的可行性和可扩展性。

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