Guiding Chaotic Swarm Dynamics in Evolving Networks of Agents with Privacy and Fairness Considerations

群集系统由许多独立的代理（机器人，人类，昆虫）组成，它们以协调的方式行动以实现共同的目标。机器人群体现在模仿社会昆虫或群鸟的行为，而人工智能代理人在社交网络中互动。随着商用无人机和自动驾驶汽车等多智能体网络系统的不断兴起，物理和网络世界中的群集行为将变得司空见惯。因此，越来越需要开发用于理解和控制群集行为的动态的方法。智能体之间的相互作用主导着群体的动态，因此了解网络连通性的影响以及它是否能引起或控制群体中的混沌是至关重要的。此外，一些代理更喜欢维护信息隐私或价值公平，这将影响群体控制协议。本计画主要是开发与研究控制群集行为的引导式网路演化游戏的动力学。应用包括控制医疗应用中的微观机器人群和管理在线社交现象以减少负面行为。该项目为年轻的研究人员提供了有价值的跨学科培训，并针对高中生和大学生开展了有针对性的外展活动。该项目调查了网络上由于进化动力学而可能出现的复杂混沌行为，开发了动态控制技术，并研究了这些系统中代理之间的隐私和公平性相关问题。这项工作的重点是半自治代理人，相互之间在网络中，并通过进化动力学改变他们的即时混合策略，重点是群集和共识动态。将系统地在微观的、基于代理的行为和宏观的、进化方程之间建立联系，如网络复制因子和其他替代方案。控制人口是通过定期驱动的游戏矩阵管理基于网络的相互作用。还将考虑考虑到代理隐私的控制系统的动态。虽然在质量上类似于经典控制中的不可观测性，但代理隐私是不同的，因为隐私影响代理之间的动态演化，而不是高层控制输入。将采取类似的办法，在各机构之间公平分配或贡献资源。该项目的目标是建立必要的控制理论体系，以允许平衡塑造，以控制在网络中相互作用的代理群体，以确定在此设置中控制的理论极限，该奖项反映了NSF的法定使命，并通过使用基金会的智力价值进行评估，被认为值得支持和更广泛的影响审查标准。

Swarming systems are composed of many independent agents (robots, humans, insects) that act in a coordinated fashion to accomplish common goals. Robotic swarms now mimic the behavior of social insects or flocking birds, while artificially intelligent agents interact in social networks. With the continuing rise of multi-agent networked systems, such as commercial drones and autonomous vehicles, swarming behaviors in the physical and cyber worlds will become commonplace. Consequently, there is an increasing need to develop methods for understanding and controlling the dynamics of swarming behavior. Agent interactions dominate swarm dynamics, so it is critical to understand the effects of network connectedness and whether it can cause or control chaos in the swarm. Additionally, some agents will prefer to maintain information privacy, or value fairness, which will affect swarm control protocols. This project is concerned with developing and studying the dynamics of guided networked evolutionary games for controlling swarming behavior. Applications include controlling microscopic robot swarms in medical applications and managing online social phenomena to decrease negative behaviors. The project provides valuable interdisciplinary training for young researchers, and targeted outreach activities towards high school students and undergraduates.This project investigates the complex chaotic behaviors that can emerge as a result of evolutionary dynamics on networks, develops techniques for dynamic control, and studies the problems associated with privacy and fairness among agents in these systems. The work focuses on semi-autonomous agents that interact with each other in a network and alter their instantaneous mixed strategies through evolutionary dynamics, with an emphasis on flocking and consensus dynamics. Connections will be made systematically between microscopic, agent-based behaviors, and macroscopic, evolutionary equations, such as the network replicator and other alternatives. Control of the population is accomplished by periodic actuation of the game matrix governing the network-based interactions. The dynamics of control systems that take agent privacy into consideration will also be considered. While qualitatively similar to non-observability in classical control, agent privacy is distinct because privacy affects the dynamic evolution among agents rather than the high-level control inputs. A similar approach will be taken to the fair allocation or contribution of resources among agents. The goals of this project are to establish the control-theoretic preliminaries necessary to allow equilibrium shaping in order to control populations of agents interacting in a network, to determine the theoretical limits of control in this setting, and to understand the impact agent privacy has on system evolution.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.