比较两种将随机性引入混合自动机的方法
Comparing Two Approaches to Include Stochasticity in Hybrid Automata
AI总结：
本文形式化并比较了分解调度与组合调度两种混合自动机随机扩展方法，分析其表达能力关系并对现有随机扩展进行分类。
摘要：
过去已经提出了混合自动机的不同随机扩展，但它们之间的表达能力关系尚不明确。为了构建并关联这些建模语言，本文形式化了两种扩展混合自动机的替代方法，使其包含离散事件及其时间点的随机选择。第一种方法，我们称之为分解调度（decomposed scheduling），通过随机竞争（stochastic races）引入随机性，为可能的离散事件选择随机时间点，并执行具有最早时间的获胜者。相比之下，组合调度（composed scheduling）首先采样下一个事件的时间点，然后在该采样时间点选择要执行的事件。我们关联了这两种方法在表达能力方面的关系，并对文献中现有的混合自动机随机扩展进行了分类。
英文摘要：
Different stochastic extensions of hybrid automata have been proposed in the past, with unclear expressivity relations between them. To structure and relate these modeling languages, in this paper we formalize two alternative approaches to extend hybrid automata with stochastic choices of discrete events and their time points. The first approach, which we call decomposed scheduling, adds stochasticity via stochastic races, choosing random time points for the possible discrete events and executing a winner with an earliest time. In contrast, composed scheduling first samples the time point of the next event and then the event to be executed at the sampled time point. We relate the two approaches regarding their expressivity and categorize available stochastic extensions of hybrid automata from the literature.
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