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Detecting and Exploiting Symmetry in Discrete-state Markov Models

机译:在离散状态马尔可夫模型中检测和利用对称性

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Dependable systems are usually designed with multiple instances of components or logical processes, and often possess symmetries that may be exploited in model-based evaluation. The problem of how best to exploit symmetry in models has received much attention from the modeling community, but no solution has garnered widespread support, primarily because each solution is limited in terms of either the types of symmetry that can be exploited or the difficulty of translating from the system description to the model formalism. We propose a new method for detecting and exploiting model symmetry in which 1) models retain the structure of the system, and 2) all symmetry inherent in the structure of the model can be detected and exploited for the purposes of state-space reduction. Composed models are constructed from models through specification of connections between models that correspond to shared state fragments. The composed model is interpreted as an undirected graph, and results from group and graph theory are used to develop procedures for automatically detecting and exploiting all symmetries in the composed model. A state-space generator which implements these algorithms within Mobius [10] is then presented.
机译:可靠的系统通常设计有多个组件或逻辑流程的实例,并且通常具有可以在基于模型的评估中被利用的对称性。关于模型中最佳对称性的问题来自模型社区的关注,但没有解决方案已经过广泛支持,主要是因为每个解决方案都是有限的,就可以被利用或翻译难度的对称类型而受到限制从系统描述到模型形式主义。我们提出了一种用于检测和利用模型对称性的新方法,其中1)模型保留系统的结构,并且2)可以检测和利用模型结构中固有的所有对称性,以用于状态空间减少的目的。编组模型由模型构成,通过对应于共享状态片段的模型之间的连接规范构建。编组模型被解释为一个无向图形,而组和图形理论的结果用于开发用于自动检测和利用组合模型中所有对称性的过程。然后呈现在Mobius [10]中实现这些算法的状态空间发生器。

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