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Optimal Training System

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA9550-05-C-0005
Agency Tracking Number: F045-014-0076
Amount: $100,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: AF04-T014
Solicitation Number: N/A
Solicitation Year: 2004
Award Year: 2005
Award Start Date (Proposal Award Date): 2004-10-06
Award End Date (Contract End Date): 2005-07-06
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
 Joseph Sullivan
5000 Forbes Avenue
Pittsburgh, PA 15213
United States

 (412) 268-1161
 Nonprofit college or university

Successful training in complex environments is normally accomplished through the interaction of a trainee and a skilled expert, but due resource constraints, experts' use in training can be problematic. Developing expert models for training is one solution, but constructing expert models is costly and time-consuming, and they tend to be difficult to validate, test, and debug. Alternatively, using an optimal model of task performance may be more efficient since optimal models are simpler to validate, test, and debug. Using a simulated task environment (STE) permits the necessary close model-trainee interaction. The simplifying assumptions of STEs often enable construction of optimal performance models, allowing them to perform the same task as the trainee using the same interface while closely observing and guiding trainee performance. We propose to develop the concept of using a normatively correct model of task performance as the core engine of an automated tutor with an initial application to a national missile defense (NMD) task STE. The NMD STE is a complex task requiring skilled operators to allocate assets under time constraints to minimize expected losses, yet is amenable to construction of an optimal model, and therefore has potential for exploring a normative modeling-based tutoring approach.

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

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