Guided Evolutionary Games for Influencing Interacting Agents' Behavior in Large Populations

群体机器人是使用大量的小型、简单的机器人来完成共同的目标，模仿在蜂巢和蜂群中自然观察到的合作任务分担。随着这项技术成为现实，人们越来越需要开发出引导和控制蜂群的方法。控制多个代理系统的标准方法依赖于将它们视为近似独立的，然而，随着这些代理的数量变得无法管理，并且它们的交互主导了它们的行为，将需要其他范例。进化博弈论在生物学和生态学中的日益成功表明，一种在种群水平上进行控制的机制，在这种机制中，个体的相互作用被操纵，以推动代理人社区走向全球目标。该奖项涉及开发和研究这些有指导的进化游戏，并在交互代理的模拟中测试它们的后果。应用包括控制医疗应用中的微型机器人群，以及对在线社会现象的引导管理，包括开发主动影响机制，以减少网络欺凌等负面在线行为。该项目将为年轻的研究人员提供有价值的跨学科培训，并针对高中生和本科生开展外联活动。该项目在弥合进化博弈论、大规模分布式机制设计和控制系统设计之间的差距方面迈出了第一步。该工作集中于通过局部通信相互作用的半自治代理，并通过使用复制者或不连续模仿动力学的进化博弈来描述他们的行为。通信要么在网络上进行，要么在欧几里得空间中进行；这两种拓扑都会增加问题的复杂性，并可能导致有组织的空间模式。我们将系统地在微观尺度、基于代理人的模拟和宏观尺度、基于密度的进化方程之间建立联系，例如复制子和其他替代方案。混合控制器的动力学也将被考虑，它改变了相互作用的主体所玩的游戏。对种群的控制是通过周期性地启动控制相互作用的游戏来实现的。本项目的目标是了解控制理论的初步知识，以允许平衡成形以控制种群，确定在这种情况下控制的理论极限，并最终将这种方法应用于高保真模拟中的自主代理的控制。

Swarm robotics is the use of a large number of small, simple robots to accomplish common goals, mimicking the cooperative task sharing observed naturally in hives and colonies. As this technology becomes a reality, there is an increasing need to develop methods for guiding and controlling the swarm. The standard approach to controlling a system of multiple agents relies on treating them as approximately separate, however other paradigms will be needed as the number of these agents becomes unmanageably large, and their interactions dominate their behavior. The growing success of evolutionary game theory in biology and ecology suggests a mechanism for control at the population level, in which individual interactions are manipulated to drive the agent community towards global goals. This award is concerned with developing and studying these guided evolutionary games, and testing their consequences in simulations of interacting agents. Applications include the control of microscopic robot swarms in medical applications and the guided management of online social phenomena, including the development of active influence mechanisms for decreasing negative online behaviors like cyberbullying. This project will provide valuable interdisciplinary training for young researchers, and target outreach activities towards high school students, and undergraduates.This project takes the first steps in bridging the gap between evolutionary game theory, massively distributed mechanism design, and the design of control systems. The work focuses on semi-autonomous agents who interact with each other through localized communication, and describes their behavior via evolutionary games using either replicator or discontinuous imitation dynamics. Communications play out either on a network or in Euclidean space; both topologies increase the problem complexity and can lead to organized spatial patterns. Connections will be made systematically between microscale, agent-based simulations, and macroscale, density-based evolutionary equations, such as the replicator and other alternatives. The dynamics of hybrid controllers, that modify the game played by the interacting agents, will also be considered. Control of the population is accomplished by periodic actuation of the game governing the interactions. The goals of this project are to understand the control-theoretic preliminaries necessary to allow equilibrium shaping in order to control populations, to determine the theoretical limits of control in this setting, and finally to apply this approach to the control of autonomous agents in high-fidelity simulations.