Artificial Intelligence for Tactical Network Troubleshooting

Abstract

The tactical network is a key component of most United States Marine Corps missions. It is critical to expeditiously stand up a robust communications architecture for both voice and data transmissions across a variety of classification levels. However, when there are unforeseen or induced faults in network configurations, the establishment time can increase by hours if not days. The research described in this report sought to determine if a large language model (LLM), when provided the correct baseline network configurations, would be able to identify errors in active working network configurations and reduce network establishment time. A/B testing was conducted to see whether teams assisted by artificial intelligence (AI) or control teams with no AI assistance could establish the network faster. The LLM hosted by NIPRGPT decreased the establishment time by 50 percent (p <0.05) compared to warfighters unaided by AI. The results conclude that AI agents such as LLMs can be useful in providing commanders with a course of action to establish command, control, communications, and computers (C4) faster.