Modeling behavior of Computer Generated Forces with Machine Learning Techniques, the NATO Task Group approach

Commercial/Military-Off-The-Shelf (COTS/MOTS) Computer Generated Forces (CGF) packages are widely used in modeling and simulation for training purposes. Conventional CGF packages often include artificial intelligence (AI) interfaces, but lack behavior generation and other adaptive capabilities. We believe Machine Learning (ML) techniques can be beneficial to the behavior modeling process, yet such techniques seem to be underused and perhaps under-appreciated. This paper aims at bridging the gap between users in academia and the military/industry at a high level when it comes to ML and AI. We address specific requirements and desired capabilities for applying machine learning to CGF behavior modeling applications. The paper is based on the work of the NATO Research Task Group IST-121 RTG-060 Machine Learning Techniques for Autonomous Computer Generated Entities.