Swarm Characteristics Classification Using Neural Networks
Donald W. Peltier III Isaac Kaminer Abram Clark Marko Orescanin
Understanding the characteristics of swarming autonomous agents is critical
for defense and security applications. This article presents a study on using
supervised neural network time series classification (NN TSC) to predict key
attributes and tactics of swarming autonomous agents for military contexts.
Specifically, NN TSC is applied to infer two binary attributes - communication
and proportional navigation - which combine to define four mutually exclusive
swarm tactics. We identify a gap in literature on using NNs for swarm
classification and demonstrate the effectiveness of NN TSC in rapidly deducing
intelligence about attacking swarms to inform counter-maneuvers. Through
simulated swarm-vs-swarm engagements, we evaluate NN TSC performance in terms
of observation window requirements, noise robustness, and scalability to swarm
size. Key findings show NNs can predict swarm behaviors with 97% accuracy using
short observation windows of 20 time steps, while also demonstrating graceful
degradation down to 80% accuracy under 50% noise, as well as excellent
scalability to swarm sizes from 10 to 100 agents. These capabilities are
promising for real-time decision-making support in defense scenarios by rapidly
inferring insights about swarm behavior.