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Aggressive AI Self-Learning Defense Network

Award Information
Agency: Department of Defense
Branch: Army
Contract: W15QKN-19-P-0033
Agency Tracking Number: A182-118-0047
Amount: $149,992.07
Phase: Phase I
Program: SBIR
Solicitation Topic Code: A18-118
Solicitation Number: 18.2
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2018-10-12
Award End Date (Contract End Date): 2020-10-10
Small Business Information
1845 West 205th Street
Torrance, CA 90501
United States
DUNS: 153865951
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Van Tri Tuc Cao
 Senior Software Engineer
 (310) 320-3088
Business Contact
 Keith Baker
Phone: (424) 835-9475
Research Institution

To address the Army’s need for an Artificial-Intelligence-based Automated Fire Control System, Physical Optics Corporation (POC) proposes to develop a new Aggressive AI Self-Learning Defense Network (ASSAILNET) technology. Specifically, the innovation in the network architecture, the network node interface design, and the network training strategy will enable each AI node in the network to interface seamlessly with a wide range of attached sensors, to share its sensor data with other nodes, to correlate shared data from other nodes with its own data, and then, together with other nodes, make coordinated collective decisions and, as a whole network, stage concerted effective actions to mitigate identified threats. As a result, this technology offers a solution to provide fire control awareness to weapons and other systems, which directly addresses the Army requirements for an AI-based Automated Fire Control System (AFCS). In Phase I, POC will demonstrate the feasibility of this technology and provide a detailed engineering design study that explains how the new technology will lead to an AI system that operates the AFCS. In Phase II, POC plans to complete the software/algorithm, update the source code, and demonstrate an AI prototype in the laboratory.

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

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