Knowledge visualization to enhance human-agent situation awareness within a computational Recognition-Primed Decision system

Recent operations in Iraq and Afghanistan have confirmed that in order to achieve effective Network-Centric Operations (NCO), innovative enhancement to military decision-making is desired. Required are processes and computational models that support the decision-makers' experience while promoting high levels of shared situation awareness (SA)-not only in the context of the external operating environment, but internally aligning the decision makers' mental model with the intelligent software agents working on their behalf. Towards this end, the aim of this research is to enhance the decision-maker's perception, comprehension, and projection of the underlying knowledge space while improving shared human-agent SA. To accomplish this we extended R-CAST, an agent-based Recognition-Primed Decision (RPD) model developed at the Pennsylvania State University (PSU) with the capability to interactively visualize the knowledge space during execution. Presented are the early results of a recently completed knowledge visualization experiment where ROTC cadets from the PSU operated the visually-enhanced R-CAST on a command and control simulation.