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Robust, Adaptive Machine Learning (RAM)

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8750-18-C-0065
Agency Tracking Number: F181-024-0465
Amount: $142,930.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: AF181-024
Solicitation Number: 2018.1
Solicitation Year: 2018
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-07-03
Award End Date (Contract End Date): 2019-07-03
Small Business Information
10440 Little Patuxent Parkway, suite 600
Columbia, MD 21044
United States
DUNS: 172216827
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Lorraine Weis
 (410) 715-0005
Business Contact
 Tom Kubancik
Phone: (410) 715-0005
Research Institution

As space becomes increasingly congested and contested space operators must rapidly assess threats with confidence to know what actions can be taken. Machine learning (ML) offers promise in efficiently dealing with these highly complex systems, however a major challenge is producing ML systems which are both robust and adaptable. Applied Defense Solutions (ADS) and the University of Texas at Austin propose to develop a Robust Adaptive Machine Learning (RAM) architecture for supporting decision making processes in the context of space battle management command and control. ADS will utilize ML techniques, both supervised and unsupervised, such as automated structure learning, physics guided data science, and active learning with user feedback. ADS will develop an architecture with predictive capability, based on past observation of patterns of life, with the ability to build relational correlations between disparate sources of data. ADS proposes five demonstration use cases to apply this RAM architecture. Training RAM algorithms requires large quantities of high quality data and ADS has access to unique space situational awareness data from its Global Optical Network.

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

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