Resource allocation under uncertainty is a classical problem in city-scale cyber-physical systems. Consider emergency response as an example; urban planners and first responders optimize the location of ambulances to minimize expected response times to incidents such as road accidents. Typically, such problems deal with sequential decision-making under uncertainty and can be modeled as Markov (or semi-Markov) decision processes. The goal of the decision-maker is to learn a mapping from states to actions that can maximize expected rewards. While online, offline, and decentralized approaches have been proposed to tackle such problems, scalability remains a challenge for real-world use-cases. We present a general approach to hierarchical planning that leverages structure in city-level CPS problems for resource allocation. We use emergency response as a case study and show how a large resource allocation problem can be split into smaller problems. We then use Monte-Carlo planning for solving the smaller problems and managing the interaction between them. Finally, we use data from Nashville, Tennessee, a major metropolitan area in the United States, to validate our approach. Our experiments show that the proposed approach outperforms state-of-the-art approaches used in the field of emergency response.

中文翻译：

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智能互联社区中动态资源分配的分层规划

不确定性下的资源分配是城市规模的网络物理系统中的经典问题。以应急响应为例；城市规划者和急救人员会优化救护车的位置，以最大限度地减少对道路事故等事件的预期响应时间。通常，此类问题涉及不确定性下的顺序决策，可以建模为马尔可夫（或半马尔可夫）决策过程。决策者的目标是学习从状态到可以最大化预期回报的动作的映射。虽然已经提出了在线、离线和分散的方法来解决这些问题，但可扩展性仍然是现实世界用例的挑战。我们提出了一种分层规划的一般方法，该方法利用城市级 CPS 问题中的结构进行资源分配。我们使用应急响应作为案例研究，并展示了如何将大型资源分配问题分解为较小的问题。然后我们使用蒙特卡洛规划来解决较小的问题并管理它们之间的交互。最后，我们使用来自美国主要大都市区田纳西州纳什维尔的数据来验证我们的方法。我们的实验表明，所提出的方法优于应急响应领域中使用的最新方法。