Networking the Boids is More Robust Against Adversarial Learning
This work addresses swarm robustness for applications like robotics or defense, but it is incremental as it modifies an existing model rather than introducing a new paradigm.
The study tackled the problem of swarm behavior by replacing the classic sensory-based neighborhood definition in Boids models with a graph-theoretic network approach, resulting in faster swarming, higher formation quality, and increased robustness against adversarial learning.
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.