Framework for Intelligent Simulation Command with Hierarchically Embedded Reinforcement Learning (FISCHER)

Wargaming plays a critical role in the modern military, both as a decision support tool for command and control centers and as a training tool for developing the future force. In both of these applications it is valuable to have access to highly skilled automated actors. In a decision support context, this facilitates fast, high fidelity simulations with a variety of possible battlefield conditions. In a training context, this supports interactive learning tools and allows commanders to hone their skills through competition with difficult opponents. Historically, Artificial Intelligence agents that can match or beat expert human performance have been limited to turn-based games. However, due to the success of IBM’s Deep Blue system at Chess, and more recently, DeepMind’s AlphaGo system at Go, the latest research has begun to investigate if the techniques employed by these systems can be improved to achieve expert-level performance at less well-defined, real-time games. To manage the fundamental complexities that military-domain AI systems face, DECISIVE ANALYTICS Corporation proposes the Framework for Intelligent Simulation Command with Hierarchically Embedded Reinforcement Learning (FISCHER). FISCHER will leverage the latest developments in Game Theory and Reinforcement Learning to provide a wargaming artificial intelligence that can perform at a humanlike level.