具有不确定性和决策的条件简单时间网络

Time Pub Date : 2019-12-01 DOI:10.4230/LIPIcs.TIME.2017.23
Matteo Zavatteri
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引用次数: 43

摘要

具有不确定性的条件简单时态网络(CSTNU)是一种能够对受条件约束和不确定时间约束的时态计划进行建模的形式化方法。这两个特征的结合代表了网络中不可控的部分。也就是说,在网络开始执行之前,我们并不完全知道将考虑哪些时间点和约束,也不知道不确定的持续时间将持续多长时间。动态可控性是指存在一种根据不可控部分的行为实时调度网络时间点的策略。尽管如此,CSTNUs未能对时间计划进行建模,其中一些条件约束是可控的,因此可能影响(或受)不可控部分。为了弥补这一差距,本文提出了具有不确定性和决策的条件简单时态网络(CSTNUDs),该网络在规范中引入决策时间点,以便在控制下对条件部分进行操作。我们将CSTNUD的动态可控性检查(DC-checking)建模为一个二人博弈,其中每个玩家在特定的时间瞬间轮流采取行动。我们给出了一个编码到定时游戏自动机的声音和完整的dc检查。我们还综合了被证明是DC的cstnud的无内存执行策略,并使用我们为cstnud设计的工具进行了实验评估,使该方法完全自动化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Conditional Simple Temporal Networks with Uncertainty and Decisions
Abstract A Conditional Simple Temporal Network with Uncertainty (CSTNU) is a formalism able to model temporal plans subject to both conditional constraints and uncertain durations. The combination of these two characteristics represents the uncontrollable part of the network. That is, before the network starts executing, we do not know completely which time points and constraints will be taken into consideration nor how long the uncertain durations will last. Dynamic Controllability (DC) implies the existence of a strategy scheduling the time points of the network in real time depending on how the uncontrollable part behaves. Despite all this, CSTNUs fail to model temporal plans in which a few conditional constraints are under control and may therefore influence (or be influenced by) the uncontrollable part. To bridge this gap, this paper proposes Conditional Simple Temporal Networks with Uncertainty and Decisions (CSTNUDs) which introduce decision time points into the specification in order to operate on this conditional part under control. We model the dynamic controllability checking (DC-checking) of a CSTNUD as a two-player game in which each player makes his moves in his turn at a specific time instant. We give an encoding into timed game automata for a sound and complete DC-checking. We also synthesize memoryless execution strategies for CSTNUDs proved to be DC and carry out an experimental evaluation with , a tool that we have designed for CSTNUDs to make the approach fully automated.
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