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Data Science Techniques for Various Mission Planning Processes and Performance Validation

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-19-C-0763
Agency Tracking Number: N19B-029-0010
Amount: $140,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N19B-T029
Solicitation Number: 19.B
Timeline
Solicitation Year: 2019
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-09-04
Award End Date (Contract End Date): 2020-03-04
Small Business Information
3527 Beverly Glen Blvd.
Sherman Oaks, CA 91423
United States
DUNS: 124668711
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Dr. Tim Chabuk Dr. Tim Chabuk
 Senior Research Scientist
 (571) 235-5720
 timc@percsolutions.com
Business Contact
 Gershon Weltman
Phone: (818) 788-1025
Email: gweltman@percsolutions.net
Research Institution
 North Carolina State University
 Dr. William Rand Dr. William Rand
 
2801 Founders Way NE
Rayleigh, NC 27695
United States

 (919) 513-7100
 Nonprofit college or university
Abstract

Mission and planning is a difficult and time-consuming process that places a heavy burden on manpower and critical thinking and is performed under significant pressure. Existing and emerging artificial intelligence (AI) and machine learning (ML) techniques are well-suited to assisting humans with these challenges. While the promise of AI/ML is great, there are significant obstacles to operationalizing AI and ML technologies for this problem, both from a technical perspective and from a human perspective. Perceptronics Solutions proposes to provide PMA-281 with our innovative solution: The Multi-Model Optimization Process (MMOP) for high impact AI/ML insertion. The MMOP system is designed to integrate a sequential set of computational and data science models that can determine where to best apply AI to improve the JMPS and subsequent mission planning process. The system generates the places where AI/ML can make the planning more efficient, lead to better outcomes and be likely to succeed by analyzing: (1) importance of a particular decision or activity is to the overall planning outcome; (2) where the system architecture and available data will make the implementation of AI/ML feasible; and (3) where it is likely AI/ML can be created to be better in critical dimensions than human decision-making.

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

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