The current airborne ISR (Intelligence, Surveillance, and Reconnaissance) collection planning and management is designed for the employment of a limited number of large ISR platforms with respect to a set of collection requirements created through an assembly line-like process. This process is often very cumbersome and mentally exhaustive for intelligence analysts when dealing with a large volume of dynamic all-source intelligence. Consequently, more human time and resources are spent on processing and digesting raw data, instead of strategizing and designing the most effective ISR collection plans. These problems could be amplified significantly in potential future conflicts with a near-peer adversary because the complexity, scale, and intensity of an airborne ISR operation could be several orders higher. This paper discusses our new research concepts and preliminary results in applying artificial intelligence (AI) and machine learning (ML) as proof-of-concepts for facilitating future ISR collection planning and management. We explored a dynamic knowledge graph to represent historical and current all-source intelligence data as well as the corresponding contextual relationships and temporal states. Such an accumulative knowledge base allows us to apply machine learning and other analytical methods to infer and visualize multi-layered intelligence on an adversary’s capabilities and operations. These analytical results could assist human ISR analysts in understanding adversary’s operational tactics, techniques, and procedures. We further look into AI-enabled virtual agents in assisting mission planners to best manage a group of ISR assets for fulfillment of collection requirements. Applying deep reinforcement learning to Intellection, a new cooperative multi-player ISR collection board game, we train the virtual agents into AI players who can follow the game rules and estimate the desirable flight paths for potentially maximizing successful collections over prioritized targets. Together, these explorations may potentially create decision advantages and relieve possible manpower bottlenecks in future fastpaced ISR collection planning and management.
|
