| dc.description.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. | en_US |
