Adaptive Weapons-to-targets Optimization Utilizing Trusted AI & Machine Learning

The primary critical technology area of this effort is Autonomous Systems in multi-domain weapons systems.  Only an autonomous system can tend to the actionable information to provide the real-time assignment of weapons to targets in an adaptive manner.  Our proposed approach relies heavily on and directly impacts the related areas of Machine Learning, Trusted AI, Human-Machine Interfaces, Integrated Sensing, and Cross-Domain Kill Chains. The synergistic effect of this multi-area integrated approach is essential to meet the demands of adaptively optimizing weapons-to-targets assignments in real-time across the joint all domain (JAD) environment.  As a representative mission set that spans JAD, we will examine the related complex missions of Offensive/Defensive Counter Air (OCA/DCA).  This effort will examine the above and other applicable technologies and within the scope of Phase I, perform limited testing of select technologies to evaluate their promise in impacting these missions, and the overall capability to perform adaptive weapons-to-targets optimization in the multi-domain environment. For Phase I our team will analyze/modify our initial assumptions as to primary technical areas of R&D identified here and answer the following questions to determine the feasibility of the proposed research. To fully explore technologies that enable Adaptive (Autonomous) Weapons-to-targets Optimization (AWTO) Utilizing Trusted AI & Machine Learning, we will initially focus our search and investigation in the following areas: Human Autonomy Interface (HAI); Trusted AI/ML; Autonomous Sensor-to-Fused Tracking and Targeting; Adversary Intent; and Underlying AI/ML Technologies.