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Optimal Training System

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA9550-05-C-0168
Agency Tracking Number: F045-014-0076
Amount: $345,183.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: AF04-T014
Solicitation Number: N/A
Solicitation Year: 2004
Award Year: 2005
Award Start Date (Proposal Award Date): 2005-08-19
Award End Date (Contract End Date): 2007-08-19
Small Business Information
4949 Pearl East Circle, Suite 300
Boulder, CO 80301
United States
DUNS: 147274237
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Brad Best
 Staff Research Scientist
 (303) 442-6947
Business Contact
 Susan Archer
Title: Director of Operations
Phone: (303) 442-6947
Research Institution
 Susan Burkett
5000 Forbes Avenue, WH 405
Pittsburgh, PA 15213
United States

 (412) 268-8746
 Nonprofit college or university

Successful training in complex environments is normally accomplished through the interaction of a trainee and a skilled expert, but due to resource constraints, experts’ use in training can be problematic. Using an optimal model of task performance subject to human constraints may be a more efficient way to develop models of skilled human performance for use in training, especially since optimal models are simpler to validate, test, and debug than corresponding expert models. In addition, constrained optimal models can be constructed in domains where no experts are available or even exist. Using a simulated task environment (STE) permits the necessary close model-trainee interaction by enabling the construction of optimal performance models and by allowing them to perform the same task as the trainee using the same interface while closely observing and guiding trainee performance. We propose to extend our previous work using a normatively correct model of task performance as the core engine of an automated tutor for a national missile defense (NMD) task STE to the more complex domain of unmanned aerial vehicle (UAV) control and mission execution. This work will integrate the functionality of planning systems with human planning constraints obtained from a study of online classroom learning.

* Information listed above is at the time of submission. *

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