结合运行时信息的近最优反应性合成。

Suda Bharadwaj, Abraham P Vinod, Rayna Dimitrova, Ufuk Topcu
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引用次数: 1

摘要

考虑最优反应性综合问题——在动态环境中计算满足任务规范的策略,并对性能指标进行优化。我们将任务关键型信息(仅在运行时可用)合并到策略综合中,以提高性能。利用这种时变信息的现有方法需要在线重新合成,这在实时应用中计算上是不可行的。在本文中,我们预先合成了一组对应于候选实例(预先指定的代表性信息场景)的策略。然后,我们提出了一种新的切换机制,在保证满足所有安全性和活动性目标的情况下,在运行时动态切换策略。我们还描述了性能次优性的边界。我们通过两个例子展示了我们的方法——机器人运动规划,其中机器人目标位置的可能性是实时更新的,以及城市空中交通的空中交通管理问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Near-Optimal Reactive Synthesis Incorporating Runtime Information.

We consider the problem of optimal reactive synthesis - compute a strategy that satisfies a mission specification in a dynamic environment, and optimizes a performance metric. We incorporate task-critical information, that is only available at runtime, into the strategy synthesis in order to improve performance. Existing approaches to utilising such time-varying information require online re-synthesis, which is not computationally feasible in real-time applications. In this paper, we pre-synthesize a set of strategies corresponding to candidate instantiations (pre-specified representative information scenarios). We then propose a novel switching mechanism to dynamically switch between the strategies at runtime while guaranteeing all safety and liveness goals are met. We also characterize bounds on the performance suboptimality. We demonstrate our approach on two examples - robotic motion planning where the likelihood of the position of the robot's goal is updated in real-time, and an air traffic management problem for urban air mobility.

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CiteScore
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