Intelligent Agents for Real-Time Story Adaptation for Training Assessment

Award Information
Agency:
Department of Defense
Branch
Army
Amount:
$119,905.00
Award Year:
2005
Program:
SBIR
Phase:
Phase I
Contract:
N61339-05-C-0033
Agency Tracking Number:
A043-226-2641
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
SOAR TECHNOLOGY, INC.
3600 Green Court, Suite 600, Ann Arbor, MI, 48105
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
036081185
Principal Investigator:
Amy Henninger
Senior Scientist
(407) 207-2237
amy@soartech.com
Business Contact:
James Rosbe
President
(734) 327-8000
rosbe@soartech.com
Research Institution:
n/a
Abstract
Simulation-based training systems are developed for a broad range of warfighter and warfighter support activities, from clearing buildings to high-level command decision-making. These systems are expensive to build and they are expensive to operate. This proposal outlines a plan to decrease both types of costs, the costs to develop training systems and the costs to operate training systems. It addresses the former through the development of a general architecture for interactive storytelling in game-based simulation environments. It addresses the latter by automating the creation of scenarios appropriate to training goals, dynamically altering these scenarios to promote the training objectives, and intelligently generating after action explanations of trainee behavior. To accomplish these technical objectives, we leverage and extend tools (storytelling architectures and automated AAR systems) previously developed through U.S. Army funding. The extension of the storytelling architecture provides for a more robust architecture that can accommodate a number of different training simulation environments. The extension of the automated AAR tool provides for a novel type of feedback possible only to environments with storytelling capability. Finally, the integration of the two provides sophisticated, knowledge-intensive, agent-based instruction both during and after an exercise. This work not only applies state-of-the-art, but it extends state-of-the-art.

* information listed above is at the time of submission.

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