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

影响大量群体中相互作用主体行为的引导进化博弈

• 批准号: 1463482
• 负责人: Andrew Belmonte
• 金额: $ 40万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1463482&HistoricalAwards=false
• 关键词: Guided; Evolutionary; Games; Influencing; Interacting

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.

群机器人是使用大量小型简单的机器人来完成共同的目标，模仿在蜂巢和殖民地中自然观察到的合作任务共享。随着这项技术成为现实，越来越需要开发用于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不可管理的大，并且它们的交互控制它们的行为，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了一种在种群水平上进行控制的机制，在这种机制中，个体之间的相互作用被操纵，以推动代理社区实现全球目标。该奖项涉及开发和研究这些引导进化游戏，并在交互代理的模拟中测试其结果。应用包括医疗应用中的微观机器人群控制和在线社会现象的指导管理，包括开发主动影响机制以减少网络欺凌等负面在线行为。该项目将为年轻研究人员提供有价值的跨学科培训，并针对高中生和大学生开展外展活动。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。这项工作的重点是半自治代理人相互作用，通过本地化的通信，并通过进化游戏使用复制或不连续的模仿动力学来描述他们的行为。通信可以在网络上进行，也可以在欧几里得空间中进行;这两种拓扑结构都增加了问题的复杂性，并可能导致有组织的空间模式。将在微观尺度，基于代理的模拟和宏观尺度，基于密度的进化方程，如复制因子和其他替代品之间进行系统的连接。混合动力控制器的动态，修改由交互代理玩的游戏，也将被考虑。种群的控制是通过控制交互的博弈的周期性驱动来实现的。该项目的目标是了解必要的控制理论的基本原理，以允许平衡塑造，以控制人口，以确定在这种设置控制的理论限制，并最终将这种方法应用到控制的自治代理高保真模拟。