Exploiting Meta-Information to Train Sensor Operators (EMITS)

A sensor operator performing tasking, processing, exploitation, and dissemination (TPED) during intelligence operations is often inadequately prepared to reason about the qualifiers of the information, or meta-information, resulting in poor situational awareness and decision-making. To address this inadequacy, we propose to design and demonstrate a system for Exploiting Meta-Information to Train Sensor Operators (EMITS). Three core components characterize our approach. First, we will perform a work domain analysis in the context of a realistic scenario to understand how expert and novice operators reason differently about meta-information, develop training scenarios addressing difficulties faced by novice operators, develop a knowledge base of sources and types of information and meta-information, and provide insight into existing training and its shortcomings. Second, we will design and demonstrate an agent-based instruction system that integrates representations of key human and non-human entities in the TPED process, intelligent training techniques that incorporate meta-information concepts, and that include user interfaces for the trainee and for the design of training scenarios. Third, we will validate our overall approach to training and develop measures for trainee assessment in the proposed system. We will leverage our team’s expertise in sensor fusion systems, meta-information concepts, and training system development to rapidly demonstrate EMITS.