Networking the Boids is More Robust Against Adversarial Learning

TL;DR

This paper introduces a graph-based networking approach for boids that enhances swarm formation speed and quality, and demonstrates increased robustness against adversarial learning compared to traditional proximity-based models.

Contribution

It proposes replacing local proximity with network-based communication in boids, improving swarm robustness and formation efficiency.

Findings

Networking the boids results in faster swarming.

Networked boids achieve higher formation quality.

Networked boids are more resistant to adversarial learning.

Abstract

Swarm behavior using Boids-like models has been studied primarily using close-proximity spatial sensory information (e.g. vision range). In this study, we propose a novel approach in which the classic definition of boids\textquoteright \ neighborhood that relies on sensory perception and Euclidian space locality is replaced with graph-theoretic network-based proximity mimicking communication and social networks. We demonstrate that networking the boids leads to faster swarming and higher quality of the formation. We further investigate the effect of adversarial learning, whereby an observer attempts to reverse engineer the dynamics of the swarm through observing its behavior. The results show that networking the swarm demonstrated a more robust approach against adversarial learning than a local-proximity neighborhood structure.