Expert Models Using (Machine) Learning to Accelerate Training system Engineering (EMULATE)

Award Information
Agency:
Department of Defense
Amount:
$99,771.00
Program:
SBIR
Contract:
W911QX-12-C-0057
Solitcitation Year:
2012
Solicitation Number:
2012.1
Branch:
Army
Award Year:
2012
Phase:
Phase I
Agency Tracking Number:
A121-026-0326
Solicitation Topic Code:
A12-026
Small Business Information
Aptima, Inc.
12 Gill Street, Suite 1400, Wo, MA, -
Hubzone Owned:
N
Woman Owned:
N
Socially and Economically Disadvantaged:
N
Duns:
967259946
Principal Investigator
 Jennifer Roberts
 Research Scientist
 (781) 496-2304
 jroberts@aptima.com
Business Contact
 Thomas McKenna
Title: Chief Financial Officer
Phone: (781) 496-2443
Email: mckenna@aptima.com
Research Institution
N/A
Abstract
Rapid development of expert models would enable the expansion of computer-based training to new training domains, but current methods for defining expert models are labor intensive, require large amounts of expert time, and often require the experts to understand aspects of the underlying technology. To reduce the amount of expert interaction required to develop a model, Aptima proposes EMULATE: Expert Models Using (Machine) Learning to Accelerate Training system Engineering (EMULATE). EMULATE"s Bayesian Inverse Reinforcement Learning (IRL) algorithms will observe experts and learn to mimic their behavior by working backward from observations of expert actions which are often fragmentary or incomplete to a representation of expert goals, preferences, and beliefs. EMULATE will feature expert model learning and data collection software modules that will integrate with simulation games and other types of intelligent tutoring system learning environments to provide feedback to students about how their behavior compares to that of experts. EMULATE will build on Aptima"s Performance Measurement Engine to collect expert data from multiple training environments and demonstrate its ability to rapidly learn expert models in three complex training domains, including multi-UAS route planning.

* information listed above is at the time of submission.

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