Guiding Chaotic Dynamics in Evolving Networked Agents with Privacy Preservation and Fairness Consideration
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.