Disaggregated Distributed AI Chat Enabler (D2ACE) System

This is a practitioner paper focused on the use of chat to communicate time-sensitive mission information within a disaggregated distributed battlespace environment. In such environments, operators may have dozens of chat conversations ongoing simultaneously. Although some of these messages are sorted according to key words to help the operators better prioritize them, these keywords are subject to typing errors and other corruption that may lead to the operator missing key decision-making information. Considering the present expansion of the battle space to include multi-mission and multi-domain, the number of chat sessions is greatly increasing. In this environment, the problem the operators face is how to reduce and prioritize the vastly expanding collection of chat messages to focus only on those chat messages that are most critical to meeting mission success and to do so accurately and dynamically. The approach presented here introduces a real-time tasking and fully autonomous system called the Disaggregated Distributed AI Chat Enabler (D2ACE). D2ACE applies AI/ML techniques to correct spelling, typos, and other corruption in chat messages, recognize uncommon language formats, and autonomously prioritize and reduce the quantity of chat messages to only those relevant to specific commander’s objectives and intent for the mission.