Robust automated knowledge capture.
Author(s) -
Susan Marie Stevens-Adams,
Robert Abbott,
James C. Forsythe,
Michael Trumbo,
Michael Joseph Haass,
Stacey M. L. Hendrickson
Publication year - 2011
Publication title -
osti oai (u.s. department of energy office of scientific and technical information)
Language(s) - English
Resource type - Reports
DOI - 10.2172/1031882
Subject(s) - computer science , task (project management) , management science , data science , artificial intelligence , systems engineering , engineering
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
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