Air Force Wants to Sift Through Simulation Data to Boost Training.

• Author: Carberry, Sean

ROTTERDAM, Netherlands -- Like the other services, the Air Force is using more and more virtual and simulated training technology, which is generating volumes of data. The Air Force Research Laboratory's Human Effectiveness Directorate is trying to determine if the right data are being collected and how to use it for maximum training effect.

There are gaps in data gathering and processing--the service needs more granular data and better tracking of a service member's training--and on the back end--it needs better artificial intelligence tools to make sense of the data, according to Summer Rebensky, a research scientist with Aptima, a contractor working for AFRL.

"We're working not only to look at live, virtual and constructive training and be able to map technologies and training experiences that best suit the training requirements, but also be able to develop training in an agile way that adapts not only to the training environments, but also to the individuals' proficiencies and needs," she said at IT2EC, one of Europe's biggest training and simulation conferences.

"Some of the simulators and training systems that we have now collect data at a very high level instead of the granularity that we really need to be able to fully leverage the AI and machine learning that's being developed today," she said.

The tools largely exist to capture the level of granularity the service needs, so it is not as much a matter of technology, but communicating needs to simulators' developers, she said.

"Whoever is funding the tools to be developed isn't necessarily the end user," she said. "We try to play an active role in connecting those two entities to ensure that we're involving them day one of the development of those tools."

That also requires looking internally to determine what data are needed. That involves interviewing subject matter experts, "seeing what data can be pulled off of these simulators, what kinds of behaviors can be observed, tracked and measured," she said.

"We're able to integrate all those measures together into measures of performance, instantiate those within simulation, refine those measures," which then creates a feedback loop, she added.

One problem with current training is that it is often pass-fail, she said. "We see performance is just...