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.