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Approximate Abstractions of Markov Chains with Interval Decision Processes ?

机译：Markov链的近似抽象与间隔决策过程 ？

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This work introduces a new abstraction technique for reducing the state space of large, discrete-time labelled Markov chains. The abstraction leverages the semantics of interval Markov decision processes and the existing notion of approximate probabilistic bisimulation. Whilst standard abstractions make use of abstract points that are taken from the state space of the concrete model and which serve as representatives for sets of concrete states, in this work the abstract structure is constructed considering abstract points that are not necessarily selected from the states of the concrete model, rather they are a function of these states. The resulting model presents a smaller one-step bisimulation error, when compared to a like-sized, standard Markov chain abstraction. We outline a method to perform probabilistic model checking, and show that the computational complexity of the new method is comparable to that of standard abstractions based on approximate probabilistic bisimulations.
机译：这项工作引入了一种新的抽象技术，用于减少大型离散时间标记的马尔可夫链的状态空间。抽象利用了间隔马尔可夫决策过程的语义和近似概率分发的现有概念。虽然标准抽象利用了从具体模型的状态空间采取的抽象点，并且作为混凝土状态的代表，在这项工作中，考虑到不一定从中选择的抽象点构建了抽象结构具体模型，而是它们是这些州的函数。与类似标准的马尔可夫链抽象相比，结果模型呈现较小的一步式双刺激误差。我们概述了一种执行概率模型检查的方法，并表明新方法的计算复杂性与基于近似概率分布的标准抽象的计算复杂性相当。

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