Robust automated knowledge capture.

Stevens-Adams, Susan Marie ; Abbott, Robert G. ; Forsythe, James Chris ; Trumbo, Michael Christopher Stefan ; Haass, Michael Joseph ; Hendrickson, Stacey M. Langfitt

Sandia National Laboratories

关键词: Decision Making;    99 General And Miscellaneous//Mathematics, Computing, And Information Science;    National Security;    Human Factors;    Automation;   

DOI  :  10.2172/1031882
RP-ID  :  SAND2011-8448
RP-ID  :  AC04-94AL85000
RP-ID  :  1031882

美国|英语

来源: UNT Digital Library

【 摘 要 】

This report summarizes research conducted through the Sandia National Laboratories Robust Automated Knowledge Capture Laboratory Directed Research and Development project. The objective of this project was to advance scientific understanding of the influence of individual cognitive attributes on decision making. The project has developed a quantitative model known as RumRunner that has proven effective in predicting the propensity of an individual to shift strategies on the basis of task and experience related parameters. Three separate studies are described which have validated the basic RumRunner model. This work provides a basis for better understanding human decision making in high consequent national security applications, and in particular, the individual characteristics that underlie adaptive thinking.