Develop and Apply Artificial Intelligence and Machine Learning Techniques for Next-Generation Mission Planning

GBL proposes the Rapid Artificially Intelligent Strike Mission Planner (RASP) to develop a layered technology approach that allows for the application of Machine Learning to this complex mission/strike planning domain. The integration of RASP with JMPS will provide a foundation for implementing Artificially Intelligent Mission Planning techniques that learn from prior mission experience/data. GBL proposes to utilize its NO4-174 EA-18G Electronic Combat Automation SBIR technologies called Electronic Combat Decision Support System as a core technology that will be leveraged. RASP will provide a Distributed Intelligent Agent-based framework (DAI) to automate development of platform mission plans. The Agent-based framework implements a publish and subscribe message system to allow for collaborative and concurrent planning amongst multiple mission planning teams across security levels while minimizing the face-to-face communication that slows the mission planning process. The decentralized capability of RASP will reduce mission planning time by allowing multiple users to simultaneously plan their role in a mission vice the waterfall method of planning that is often practiced today. The intelligent agents will communicate planning information amongst necessary users to help improve the mission planning workflow and ultimately reduce the overall time and improve automation of mission planning for multi-vehicle strike mission platforms.