Artificial Intelligence (AI) Assisted Strike Planner (AASP)

This project will design and develop concepts to incorporate deep learning and natural language processing into mission and strike planning and re-planning. First, we will determine which functions in the current Joint Mission Planning System Maritime (JMPS-M) can be productively performed in a near-autonomous manner using (a) current JMPS-M methods, (b) methods from other Navy tactical decision aids, (c) deep learning, or (d) natural language processing. Next, we will develop a data collection framework to support near-autonomous mission planning and training of artificial algorithms which are components of it. Third, we will develop methods to create and dynamically update threat maps which allow us to exploit the proven capabilities of convolutional deep neural networks to analyze images for Suppression of Enemy Air Defenses (SEAD). Option tasks will refine our overall plan and demonstrate the role natural language processing plays in mission planning.