Stochastic Control for Decentralized Systems: A Common Information Approach

工程系统涉及一系列广泛的决策问题。规划发电机的生产计划，控制飞机的运动，或选择性地激活传感器进行环境观察，都需要随着时间的推移做出多个决策，而且经常面临系统和未来的不确定性。许多技术系统的成功可归因于其潜在决策问题的成功解决。许多这些决策问题是在信息中心性和决策中心性的共同假设下解决的。然而，现代技术和经济的发展导致了越来越分散的系统。例如，智能传感器的出现导致传感和监测系统可以以分布式方式做出决策，网络的普及使个人社会和经济决策相结合，推动放松管制导致电力系统中的竞争决策。在所有这些系统中，中央指挥结构都在不同程度上被分散指挥结构所取代。这些系统未来的成功将取决于我们理解、分析和解决这些系统产生的分散决策问题的能力。拟议研究的目的是为在动态和不确定环境中运行的分散系统中出现的顺序决策问题制定一个系统框架。拟议的研究将影响分散和网络系统在不同的应用领域，包括基础设施系统，如电力，交通和通信网络，传感和监视系统，如无人驾驶飞行器或机器人团队，以及网络控制系统。该项目旨在为分散和网络系统开发随机控制的系统理论。这样的系统包括一个基于环境和/或彼此的不同信息做出决策的代理网络。在许多应用程序中，不同的决策者共享一个共同的系统范围目标，并且应该表现为同一团队的合作成员，而不是游戏中的竞争参与者。在这种具有共同目标的合作系统中，相对于博弈论的均衡识别，基于智能体决策策略的目标全局优化是更合适的解决方法。本提案的目标是为顺序的、分散的决策问题开发一种动态规划的全局优化方法。特别是，拟议的研究将研究自动驾驶车辆/代理的分散控制，分散有限状态系统的控制以及传感器网络和网络控制系统中信息流的控制。该项目还将探索分散合作系统与信息不对称参与者之间的随机博弈之间的联系。该研究的教育影响将包括为研究生提供随机控制、博弈论、优化和网络方面的多学科培训，开发新的研究生水平课程，重点关注网络系统中的分散决策，并支持女性博士生参与我们的研究项目。

Engineering systems involve a wide array of decision-making problems. Planning the production schedule for a power generator, controlling the motion of an aircraft, or selectively activating sensors to make environmental observations, all require multiple decisions to be made over time, very often in face of uncertainties about the system and the future. The success of many technological systems can be attributed to the successful solution of their underlying decision-making problems. Many of these decision-making problems were addressed under the common assumption of centrality of information and centrality of decision-making. Modern technological and economic developments, however, have led to increasingly decentralized systems. For example, the advent of smart sensors has led to sensing and monitoring systems that can make decisions in a distributed manner, the prevalence of networks has coupled individual social and economic decisions, the push for de-regulation has resulted in competitive decision-making in power systems. In all these systems, the centralized command structure is being replaced, to a varying degree, by a decentralized one. The future success of these systems will depend on our ability to understand, analyze and solve the decentralized decision-making problems these systems create. The objective of the proposed research is to develop a systematic framework for sequential decision-making problems that arise in decentralized systems operating in dynamic and uncertain environments. The proposed research will impact decentralized and networked systems in diverse application domains including infrastructure systems like power, transportation and communication networks, sensing and surveillance systems like teams of unmanned aerial vehicles or robots as well as networked control systems.This project aims to develop a systematic theory of stochastic control for decentralized and networked systems. Such systems involve a network of agents making decisions based on different information about the environment and/or about each other. In many applications, different decision makers share a common system-wide objective and should behave as cooperating members of the same team rather than competing players in a game. In such cooperative systems with a common objective, the global optimization of this objective with respect to agent decision strategies rather than game-theoretic equilibrium identification is a more appropriate solution approach. The goal of this proposal is to develop a dynamic programming like global optimization approach for sequential, decentralized decision-making problems. In particular, the proposed research will investigate decentralized control of autonomous vehicles/agents, control of decentralized finite state systems and control of information flow in sensor networks and networked control systems. The project will also explore the connections between decentralized cooperative systems and stochastic games among players with asymmetric information. The educational impact of the proposed research will include providing graduate students with a multi- disciplinary training in stochastic control, game theory, optimization and networks, development of a new graduate level course focusing on decentralized decision-making in networked systems and supporting women PhD students in our research program.