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Autonomous Decision Making via Hierarchical Brain Emulation -- 19-009

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8750-19-C-0204
Agency Tracking Number: F19A-009-0067
Amount: $147,707.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: AF19A-T009
Solicitation Number: 19.A
Solicitation Year: 2019
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-08-29
Award End Date (Contract End Date): 2020-08-29
Small Business Information
1818 Library Street Suite 600
Reston, VA 20190
United States
DUNS: 107939233
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Kristine Bell
 Principal Investigator - Sr. Analyst II
 (703) 326-2913
Business Contact
 Seth Blackwell
Phone: (703) 326-2907
Research Institution
 The Ohio State University
 Dr. Graeme Smith Dr. Graeme Smith
1330 Kinnear Road
Columbus, OH 43212
United States

 (614) 292-1664
 Nonprofit College or University

The objective of this project is to develop human intelligence-inspired algorithms that exploit multi-modal sources of low and high quality data to achieve a series of objectives such as detection, localization, tracking, and classification. A Bayesian model-based hierarchical adaptive decision making (HADM) algorithm will be developed which includes multiple levels of decision making organized in a hierarchical manner, a confidence factor associated with each decision, and a feedback mechanism used to trigger the need for higher quality data or to go back and correct erroneous intermediary decisions. A drawback of the Bayesian model-based approach is that the models required by the algorithm may not always be known or may be difficult to work with analytically. To overcome this limitation, an HADM algorithm that uses a model discovery-based approach to learn the required models from the data will also be developed using the exponentially embedded families approach to probability density function modeling and feature selection. Performance will be demonstrated on simulated radar and image data, as well as experimentally collected data from a laboratory testbed.

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

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