TY - JOUR
T1 - Human-agent collaboration for time-stressed multicontext decision making
AU - Fan, Xiaocong
AU - McNeese, Michael
AU - Sun, Bingjun
AU - Hanratty, Timothy
AU - Allender, Laurel
AU - Yen, John
N1 - Funding Information:
Manuscript received March 23, 2007; revised April 10, 2009. First published December 4, 2009; current version published February 18, 2010. This work was supported by the Army Research Laboratory’s Advanced Decision Architectures Collaborative Technology Alliance as an FY06 Research Task. This paper was recommended by Editor W. Pedrycz.
Funding Information:
Dr. Yen is a member of the editorial board of several international journals on intelligent systems. He is the recipient of a National Science Foundation Young Investigator Award in 1992.
PY - 2010/3
Y1 - 2010/3
N2 - Multicontext team decision making under time stress is an extremely challenging issue faced by various real-world application domains. In this paper, we employ an experience-based cognitive agent architecture (called R-CAST) to address the informational challenges associated with military command and control (C2) decision-making teams, the performance of which can be significantly affected by dynamic context switching and tasking complexities. Using context switching frequency and task complexity as two factors, we conducted an experiment to evaluate whether the use of R-CAST agents as teammates and decision aids can benefit C2 decision-making teams. Members from a U.S. Army Reserve Officer Training Corps organization were randomly recruited as human participants. They were grouped into ten humanhuman teams, each composed of two participants, and ten humanagent teams, each composed of one participant and two R-CAST agents, as teammates and decision aids. The statistical inference of experimental results indicates that R-CAST agents can significantly improve the performance of C2 teams in multicontext decision making under varying time-stressed situations.
AB - Multicontext team decision making under time stress is an extremely challenging issue faced by various real-world application domains. In this paper, we employ an experience-based cognitive agent architecture (called R-CAST) to address the informational challenges associated with military command and control (C2) decision-making teams, the performance of which can be significantly affected by dynamic context switching and tasking complexities. Using context switching frequency and task complexity as two factors, we conducted an experiment to evaluate whether the use of R-CAST agents as teammates and decision aids can benefit C2 decision-making teams. Members from a U.S. Army Reserve Officer Training Corps organization were randomly recruited as human participants. They were grouped into ten humanhuman teams, each composed of two participants, and ten humanagent teams, each composed of one participant and two R-CAST agents, as teammates and decision aids. The statistical inference of experimental results indicates that R-CAST agents can significantly improve the performance of C2 teams in multicontext decision making under varying time-stressed situations.
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U2 - 10.1109/TSMCA.2009.2035302
DO - 10.1109/TSMCA.2009.2035302
M3 - Article
AN - SCOPUS:77249152145
SN - 1083-4427
VL - 40
SP - 306
EP - 320
JO - IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans
JF - IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans
IS - 2
M1 - 5345826
ER -
