基于流动管道构造的受限类随机混合自动机可达概率优化

IF 0.7 4区 计算机科学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Carina da Silva, Stefan Schupp, Anne Remke
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引用次数: 0

摘要

随机混合自动机(SHA)是评估关键基础设施可靠性和安全性的有力工具。然而,在许多纯混合模型中存在的不确定性的解决通常只在SHA中隐式地考虑。本文提出了一种计算具有紧急过渡和随机时钟的奇异自动机的最大和最小可达概率的算法,这些自动机遵循任意连续概率分布。我们借鉴了混合系统可达性分析中众所周知的方法,即流动管道构造,然后将其扩展到随机变量存在下的非确定性优化。首先,从计算的流管中提取保证特定目标状态可达性的随机时钟值,然后对这些值进行积分计算可达性概率。我们使用基于集合的方法计算历史依赖的预言性和非预言性调度程序的最大和最小概率。详细讨论了HyPro库的实现以及该方法的复杂性。两个具有不确定性选择的案例研究表明了该方法的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimizing reachability probabilities for a restricted class of Stochastic Hybrid Automata via Flowpipe-Construction

Stochastic hybrid automata (SHA) are a powerful tool to evaluate the dependability and safety of critical infrastructures. However, the resolution of nondeterminism, which is present in many purely hybrid models, is often only implicitly considered in SHA. This paper instead proposes algorithms for computing maximum and minimum reachability probabilities for singular automata with urgent transitions and random clocks which follow arbitrary continuous probability distributions. We borrow a well-known approach from hybrid systems reachability analysis, namely flowpipe construction, which is then extended to optimize nondeterminism in the presence of random variables. Firstly, valuations of random clocks which ensure reachability of specific goal states are extracted from the computed flowpipes and secondly, reachability probabilities are computed by integrating over these valuations. We compute maximum and minimum probabilities for history-dependent prophetic and non-prophetic schedulers using set-based methods. The implementation featuring the library HyPro and the complexity of the approach are discussed in detail. Two case studies featuring nondeterministic choices show the feasibility of the approach.

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来源期刊
ACM Transactions on Modeling and Computer Simulation
ACM Transactions on Modeling and Computer Simulation 工程技术-计算机:跨学科应用
CiteScore
2.50
自引率
22.20%
发文量
29
审稿时长
>12 weeks
期刊介绍: The ACM Transactions on Modeling and Computer Simulation (TOMACS) provides a single archival source for the publication of high-quality research and developmental results referring to all phases of the modeling and simulation life cycle. The subjects of emphasis are discrete event simulation, combined discrete and continuous simulation, as well as Monte Carlo methods. The use of simulation techniques is pervasive, extending to virtually all the sciences. TOMACS serves to enhance the understanding, improve the practice, and increase the utilization of computer simulation. Submissions should contribute to the realization of these objectives, and papers treating applications should stress their contributions vis-á-vis these objectives.
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