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

蜂群系统由许多独立的主体(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群居昆虫或成群的鸟类的行为，而人工智能代理在社交网络中相互作用。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崛起，物理和网络世界中的蜂拥而至行为将变得司空见惯。因此，越来越需要开发方法来了解和控制蜂群行为的动力学。主体之间的相互作用主导着群体动力学，因此理解网络连通性的影响以及它是否会导致或控制群体中的混沌是至关重要的。此外，一些代理将倾向于维护信息隐私或价值公平，这将影响群控制协议。本项目致力于开发和研究用于控制蜂群行为的引导式网络进化游戏的动力学。应用包括控制医疗应用中的微型机器人群，以及管理在线社会现象以减少负面行为。该项目为年轻研究人员提供了有价值的跨学科培训，并针对高中生和本科生开展了有针对性的推广活动。该项目调查了网络进化动力学可能产生的复杂混沌行为，开发了动态控制技术，并研究了这些系统中与代理之间的隐私和公平相关的问题。该工作集中于半自治智能体，这些智能体在网络中相互作用，并通过进化动力学改变其瞬时混合策略，重点是集群和共识动力学。系统地将在微观的、基于代理的行为和宏观的进化方程之间建立联系，例如网络复制者和其他替代方案。对种群的控制是通过周期性地激活管理基于网络的交互的博弈矩阵来实现的。还将考虑考虑代理隐私的控制系统的动态。虽然在定性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。将对代理人之间的资源公平分配或贡献采取类似的办法。这个项目的目标是建立必要的控制理论初步知识，以允许平衡塑造，以便控制在网络中交互的代理群体，确定在这种设置下控制的理论限制，并了解代理隐私对系统进化的影响。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.