Exploiting Artificial Intelligence to Improve After Action Reviews for the Digitized Company Team

The Close Combat Tactical Trainer (CCTT) after-action review (AAR) system produces a standard set of statistical displays that trainers may use to debrief exercise participants. These displays are difficult to interpret and do not readily reveal to the player unit "WHAT happened" during the battle. If unit players are able to discern "WHAT happened", there are no correlating displays to provide insights into "WHY it happened." During Phase I, our focus will be on the development of trainer-friendly AAR displays that provide a direct comparison of unit performance (WHAT happened) to applicable task standards. We will also examine the feasibility of using non-network data, as well as Distributed Interactive Simulation (DIS) network data, to support the production of displays that will provide the AAR audience insights into "WHY it happened." Phase I will then examine the feasibility of using an artificial intelligence technology to support automated production of each proposed CCTT AAR display. We will recommend user interface modifications, any required hardware upgrades, and will estimate the level of effort required to develop software for the recommended functionality. During Phase II we will conduct proof-of-principle demonstrations of Phase I operational and technical concepts.