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TARGET: Transfer via Active Requests to Generalize Effective Training

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-17-C-0042
Agency Tracking Number: N15A-013-0036
Amount: $999,566.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: N15A-T013
Solicitation Number: 2015.0
Solicitation Year: 2015
Award Year: 2017
Award Start Date (Proposal Award Date): 2017-03-09
Award End Date (Contract End Date): 2019-02-15
Small Business Information
3600 Green Court
Ann Arbor, MI 48105
United States
DUNS: 009485124
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Dr. Jeremiah Folsom-Kovarik
 Research Scientist
 (407) 249-0454
Business Contact
 Andrew Dallas
Phone: (734) 887-7603
Research Institution
 University of California, Davis
 Dr. Ian Davidson
UC - Davis Davis One Shields Avenue
Davis, CA 95916
United States

 (530) 752-5764
 Nonprofit College or University

Active Transfer Learning (ATL) is a machine learning approach that produces excellent accuracy and predictive power while requiring much less input data than competing approaches. SoarTech, partnered with University of California Davis, has used ATL to improve the understanding of skills and skill relationships in the Navys tactics and decision-making assessment system, DARTS. SoarTech showed in Phase I that ATL let DARTS accurately estimate mastery of sixteen different skills after inputs of only five student data points. We implemented a working prototype and evaluated it with a series of historical and simulated datasets.During Phase II, SoarTech and partners will extend the Phase I research by enhancing the integration of ATL with the DARTS system, addressing limitations in the state of the art specific to understanding complex operational skills, and using ATL to enable new capabilities in training and personnel management such as crowdsourcing to capture new knowledge and updates from operational users. We will evaluate and validate the usefulness of the ATL approach in a series of studies using simulated students and human users in the Option period. The research will lead to new, more detailed, and more frequently updated understanding of skills for training and personnel management experts.

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

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