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Proceedings Paper

A self-adapting heuristic for automatically constructing terrain appreciation exercises
Author(s): S. Nanda; C. L. Lickteig; P. S. Schaefer
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Paper Abstract

Appreciating terrain is a key to success in both symmetric and asymmetric forms of warfare. Training to enable Soldiers to master this vital skill has traditionally required their translocation to a selected number of areas, each affording a desired set of topographical features, albeit with limited breadth of variety. As a result, the use of such methods has proved to be costly and time consuming. To counter this, new computer-aided training applications permit users to rapidly generate and complete training exercises in geo-specific open and urban environments rendered by high-fidelity image generation engines. The latter method is not only cost-efficient, but allows any given exercise and its conditions to be duplicated or systematically varied over time. However, even such computer-aided applications have shortcomings. One of the principal ones is that they usually require all training exercises to be painstakingly constructed by a subject matter expert. Furthermore, exercise difficulty is usually subjectively assessed and frequently ignored thereafter. As a result, such applications lack the ability to grow and adapt to the skill level and learning curve of each trainee. In this paper, we present a heuristic that automatically constructs exercises for identifying key terrain. Each exercise is created and administered in a unique iteration, with its level of difficulty tailored to the trainee's ability based on the correctness of that trainee's responses in prior iterations.

Paper Details

Date Published: 15 April 2008
PDF: 8 pages
Proc. SPIE 6961, Intelligent Computing: Theory and Applications VI, 69610G (15 April 2008); doi: 10.1117/12.780340
Show Author Affiliations
S. Nanda, SDS International, Inc. (United States)
C. L. Lickteig, Army Research Institute (United States)
P. S. Schaefer, Army Research Institute (United States)


Published in SPIE Proceedings Vol. 6961:
Intelligent Computing: Theory and Applications VI
Kevin L. Priddy; Emre Ertin, Editor(s)

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