Swarm Characteristic Classification using Robust Neural Networks with Optimized Controllable Inputs

2025-02-05

AI Summary

Tactical identification of adversarial swarms under high uncertainty remains challenging due to highly dynamic group behaviors, severe measurement noise, and scarce prior knowledge. Method: We propose a robust tactical classification framework tailored for military confrontation scenarios. It constructs an augmented dataset with multi-dimensional variations—defender count, motion patterns, and noise levels—and introduces a “Controllable Input Optimization” (CIO) paradigm: during inference, defender trajectories are jointly optimized to actively elicit discriminative adversarial responses; this is integrated with uncertainty-aware data augmentation and constraint-aware trajectory optimization to enhance generalization of time-series classifiers. Contribution/Results: This work pioneers the integration of controllable inputs into a closed-loop tactical classification system. It significantly improves classification accuracy and confidence under strong noise and dynamic adversarial conditions, while enabling resource-efficient deployment and satisfying physical trajectory constraints.

Technology Category

• Search and Optimization: Adversarial Search
• Machine Learning: Adversarial Learning & Robustness
• Multiagent Systems: Adversarial Agents

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Classify swarm characteristics with robust neural networks.
• Enhance NN accuracy using enriched, varied operational datasets.
• Optimize defender trajectories for effective tactic classification.

Innovation

• Robust Neural Networks
• Optimized Controllable Inputs
• Enhanced Classification Accuracy