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arXiv 2104.02172eess.SYcs.SY
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部分已知切换随机系统的策略综合

Strategy Synthesis for Partially-known Switched Stochastic Systems

John Jackson, Luca Laurenti, Eric Frew, Morteza Lahijanian

摘要:

我们提出了一个数据驱动的框架,部分已知的切换随机系统的策略综合。系统的属性指定使用线性时序逻辑(LTL)在有限的痕迹(LTLf),这是表达LTL,并使有限的行为解释。该框架首先通过高斯过程回归学习未知动态。然后,它建立了一个正式的抽象的切换系统的不确定性马尔可夫模型,即一个区间马尔可夫决策过程(IMDP),通过会计系统的随机行为和学习步骤中的不确定性。然后,我们合成一个策略,最大限度地提高LTLf规范的满意度概率,并对抽象中的所有不确定性具有鲁棒性。然后将该策略细化为原随机系统的切换策略。我们表明,这种策略是接近最优的,并提供了一个约束的距离(错误)的最优策略。我们实验验证我们的框架在各种案例研究,包括线性和非线性切换随机系统。

英文摘要:

We present a data-driven framework for strategy synthesis for partially-known switched stochastic systems. The properties of the system are specified using linear temporal logic (LTL) over finite traces (LTLf), which is as expressive as LTL and enables interpretations over finite behaviors. The framework first learns the unknown dynamics via Gaussian process regression. Then, it builds a formal abstraction of the switched system in terms of an uncertain Markov model, namely an Interval Markov Decision Process (IMDP), by accounting for both the stochastic behavior of the system and the uncertainty in the learning step. Then, we synthesize a strategy on the resulting IMDP that maximizes the satisfaction probability of the LTLf specification and is robust against all the uncertainties in the abstraction. This strategy is then refined into a switching strategy for the original stochastic system. We show that this strategy is near-optimal and provide a bound on its distance (error) to the optimal strategy. We experimentally validate our framework on various case studies, including both linear and non-linear switched stochastic systems.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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