Swarm Characteristic Classification using Robust Neural Networks with Optimized Controllable Inputs

TL;DR

This paper develops a robust neural network approach for classifying swarm tactics under uncertainty, using optimized defender trajectories to improve accuracy and operational flexibility in military scenarios.

Contribution

It introduces a method to enhance neural network robustness through enriched datasets and a framework for optimal defender trajectory design to improve classification in uncertain environments.

Findings

Robust neural networks outperform standard models in uncertain conditions.

Enriched datasets improve classification accuracy and operational flexibility.

Optimized defender trajectories increase the likelihood of correct tactic classification.

Abstract

Having the ability to infer characteristics of autonomous agents would profoundly revolutionize defense, security, and civil applications. Our previous work was the first to demonstrate that supervised neural network time series classification (NN TSC) could rapidly predict the tactics of swarming autonomous agents in military contexts, providing intelligence to inform counter-maneuvers. However, most autonomous interactions, especially military engagements, are fraught with uncertainty, raising questions about the practicality of using a pretrained classifier. This article addresses that challenge by leveraging expected operational variations to construct a richer dataset, resulting in a more robust NN with improved inference performance in scenarios characterized by significant uncertainties. Specifically, diverse datasets are created by simulating variations in defender numbers, defender motions, and measurement noise levels. Key findings indicate that robust NNs trained on an enriched dataset exhibit enhanced classification accuracy and offer operational flexibility, such as reducing resources required and offering adherence to trajectory constraints. Furthermore, we present a new framework for optimally deploying a trained NN by the defenders. The framework involves optimizing defender trajectories that elicit adversary responses that maximize the probability of correct NN tactic classification while also satisfying operational constraints imposed on the defenders.