TY - GEN
T1 - Knowledge visualization to enhance human-agent situation awareness within a computational recognition-primed decision system
AU - Hanratty, Timothy
AU - Hammeil, Robert J.
AU - Yen, John
AU - McNeese, Michael
AU - Oh, Sooyoung
AU - Kim, Hyun Woo
AU - Minotra, Dev
AU - Strater, Laura
AU - Cuevas, Haydee
AU - Colombo, Dan
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
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U2 - 10.1109/MILCOM.2009.5379847
DO - 10.1109/MILCOM.2009.5379847
M3 - Conference contribution
AN - SCOPUS:77951448624
SN - 9781424452385
T3 - Proceedings - IEEE Military Communications Conference MILCOM
BT - MILCOM 2009 - 2009 IEEE Military Communications Conference
T2 - 2009 IEEE Military Communications Conference, MILCOM 2009
Y2 - 18 October 2009 through 21 October 2009
ER -