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Abstraction Based Reachability Analysis for Finite Branching Stochastic Hybrid Systems

机译:有限分支随机混合系统基于抽象的可达性分析

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In this paper, we address the problem of computing the probability of reaching a desired set in a subclass of stochastic hybrid systems, wherein the stochasticity arises from the randomness of the initial distribution of continuous states, and the probabilistic transitions in the underlying finite state Markov chain. In particular, the continuous dynamics is deterministic for each mode and hence, there are finitely many probabilistic successors for a given state. We exploit this property and extend a partition based abstraction technique developed for finite state systems to the stochastic hybrid system setting. We prove the correctness of our algorithm by defining appropriate simulation relations that relate the stochastic hybrid system to a finite state ``generalized" probabilistic transition system that we obtain as a result of abstraction. We show that the simulation relation defined provides upper and lower bounds on the probability of reachability. In particular, tighter bounds can be obtained by refining the partition. We apply our algorithm to automatically analyze a smart home application.
机译:在本文中,我们解决了在随机混合系统的子类中计算达到期望集合的概率的问题,其中随机性来自连续状态初始分布的随机性以及底层有限状态马尔可夫中的概率跃迁链。尤其是,连续动力学对于每种模式都是确定性的,因此,给定状态的概率继任者有限。我们利用此属性并将针对有限状态系统开发的基于分区的抽象技术扩展到随机混合系统设置。我们通过定义适当的模拟关系来证明算法的正确性,该关系将随机混合系统与作为抽象结果而获得的有限状态``广义''概率转移系统相关联。我们证明定义的模拟关系提供了上下限尤其是通过优化分区可以获得更严格的界限,我们将算法应用到自动分析智能家居应用中。

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