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Proceedings Paper

Automated Knowledge Acquisition Techniques For Intelligence Analysts
Author(s): Stuart L. Crawford; Steven K. Souders; Thomas C. Fall; Marla J. Rabin
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Paper Abstract

Intelligence analysts are frequently faced with the task of developing and maintaining systems for the classification of large, noisy, incomplete and highly dynamic datasets. Until recently, analysts have had only two methodologies to bring to bear upon this task. The first requires that the analyst manually work with the data until a "feel" is developed for it. The second involves the application of classical statistical techniques such as discriminant analysis and numerical taxonomy. Unfortunately, however, these techniques often yield unintuitive decision rules and clusterings, or can demand unrealistic distributional assumptions. Because these traditional techniques are not always applicable, knowledge acquisition has been a "bottleneck" for building rule-based system. However, new automatic techniques drawn from the domain of machine learning are being developed that address both of these problems: they do not require such distributional assumptions, and they tend to deliver readily interpretable clusterings and decision rules. This paper describes experiments that explore the applicability of two such systems, MOCA and CART, as tools to help analysts cope with large quantities of intelligence data.

Paper Details

Date Published: 21 March 1989
PDF: 12 pages
Proc. SPIE 1095, Applications of Artificial Intelligence VII, (21 March 1989); doi: 10.1117/12.969259
Show Author Affiliations
Stuart L. Crawford, Advanced Decision Systems (United States)
Steven K. Souders, Advanced Decision Systems (United States)
Thomas C. Fall, Advanced Decision Systems (United States)
Marla J. Rabin, Advanced Decision Systems (United States)

Published in SPIE Proceedings Vol. 1095:
Applications of Artificial Intelligence VII
Mohan M. Trivedi, Editor(s)

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