Applying AI Deep Learning to the DOD's big simulation and training projects

Abstract

All military services have invested heavily in creating, deploying, and training with large simulation systems. Historically, these have applied artificial intelligence algorithms to controlling non-player characters (NPCs) or semi-Automated forces (SAF) during the execution phase of the events. This has allowed a few human role players to control a much larger collection of virtual/simulated entities or aggregated units. Structured algorithms like finite state machines and knowledge-based systems have typically been limited to this single execution phase of the entire training process. However, "the new AI", deep learning and machine learning algorithms, operate much differently from the previous generation. These models configure themselves (or learn) from massive amounts of collected data (both labeled and unlabeled). As such, real applications require the collection of massive historical data. Luckily, military simulation events regularly generate multiple gigabytes of data as a by-product of the training event. Previously a tiny portion of this was saved and curated to perform after action review, and the remainder was deleted since it had no practical use and consumed scarce and expensive storage. To leverage deep learning, the military services need to reinvent their policies, relationships, and processes for handling these huge volumes of previously worthless, but now priceless, data. Additionally, deep learning models are much more widely applicable than the previous generation of algorithms. DL models can learn to process huge volumes of data to contribute to the analysis or after action review stage of an exercise. They can also be used to auto generate variations on giant scenario databases. They can match exercise plans against exercise results to determine whether training objectives have been met. And they can animate the NPC and SAF units during the execution phase. We stand at the edge of the application of deep learning to every phase of large military training simulation events.