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Information and Decision Recommender

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-14-P-1226
Agency Tracking Number: N14A-024-0183
Amount: $149,949.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N14A-T024
Solicitation Number: 2014.1
Timeline
Solicitation Year: 2014
Award Year: 2014
Award Start Date (Proposal Award Date): 2014-08-04
Award End Date (Contract End Date): 2015-12-04
Small Business Information
1050 W NASA Blvd Suite 155
Melbourne, FL 32901
United States
DUNS: 000000000
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Bruce McQueary
 Founding Partner
 (321) 591-7371
 bmcqueary@securboration.com
Business Contact
 Lynn Lehman
Title: CEO
Phone: (919) 244-3946
Email: contracts@securboration.com
Research Institution
 Dartmouth College
 Shea McGovern
 
Office of Sponsored Projects 11 Rope Ferry Rd. #6210
HANOVER, NH 03755-1404
United States

 (603) 646-3007
 Nonprofit College or University
Abstract

A general framework for course of action (COA) recommender systems consists of four primary functional areas : (1) mapping human entered COA into formalized, machine readable knowledge representation; (2) deconstructing COA decision models into relevant features or variables of interest; (3) gathering and routing intelligence related to those features to specific warfighters evaluating COA; and (4) predictive capability that leverages the routed information sets to suggest COA recommendations. With this in mind, Securboration proposes to develop a predictive capability to rank COA , referred to as COA Recommendation Services (COARS) , that fits within the Navys vision for a COA recommender. COARS is not a monolithic solution, but rather a service-based implementation that specifically addresses key technology in the COA recommender vision: predictive capability to rank COA. The novelty of our Teams approach is that we leverage seminal research in computational modeling and extend it by borrowing deep learning concepts to fuse knowledge and derive complex emergent behavior that is missed with existing reasoning techniques and algorithms. This accounts for the intricacies and dynamisms of the operational environment and overcomes the limitations of current COA recommender approaches that are based on simplistic assumptions regarding adversary objectives, limitations, and intent.

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

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