Achieving Federated ISR Collection through Intelligent Optimization and Natural AI-supported Decision Operations (AFICIONADO)

Conducting timely high-quality intelligence, surveillance, and reconnaissance (ISR) in Multi-Domain Operations (MDO) requires providing Army support staff with situation awareness data and new processing and visualization tools that enable improved planning, synchronizing, and conducting of multi-intelligence, multi-platform collection. To better anticipate and address the new coordination challenges associated with MDO and demands for obtaining the highest quality ISR in scenarios, such as an adaptive enterprise, Army support staff need better precision collection orchestration and mission management capabilities. However, this requires effective and efficient planning at the unit level, a means for orchestrating joint planning across the enterprise, and developing tools specifically designed to support collection managers in their complex functions. To address these challenges, Charles River Analytics proposes to design and demonstrate the feasibility of Achieving Federated ISR Collection through Intelligent Optimization and Natural AI-supported Decision Operations (AFICIONADO). Our approach consists of: (1) architecting a collection plan recommendation engine to aid humans in optimizing plans; (2) developing an enterprise-level arbitrage agent that monitors plans generated across the federated system and identifies opportunities to improve enterprise effectiveness; and (3) designing an ecological workflow-centered human machine interface for both individual work and cross-unit coordination.