Agent-based Reduction of Information Density (ARID)

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8650-06-M-6691
Agency Tracking Number: F061-031-2528
Amount: $100,000.00
Phase: Phase I
Program: SBIR
Awards Year: 2006
Solicitation Year: 2006
Solicitation Topic Code: AF06-031
Solicitation Number: 2006.1
Small Business Information
21ST CENTURY SYSTEMS, INCORPORATED
12152 Windsor Hall Way, Herndon, VA, 20170
DUNS: 949183701
HUBZone Owned: N
Woman Owned: Y
Socially and Economically Disadvantaged: N
Principal Investigator
 Larry Solecki
 Program Manager
 (719) 457-4245
 Larry.Solecki@21csi.com
Business Contact
 Stuart Aldridge
Title: VP, Deputy CTO
Phone: (402) 505-7887
Email: stuart@21csi.com
Research Institution
N/A
Abstract
Information overload and cluttered user interfaces cause decreased situational awareness and lowered performance of the human operators. Irrelevant data increases searching times for tasks requiring the identification of threats, causing delayed decisions. Cognitive burden on the user increases as displays become more cluttered, which results in increased operator stress leading to poorer decisions. 21st Century Systems, Incorporated (21CSI) is pleased to propose to research and develop an intelligent agent-based system for the automatic de-cluttering of representational UAV command and control interfaces. 21CSI’s Agent-based Reduction of Information Density (ARID) concept encompasses the development of an ontological representation of the problem domain and user interface elements, design of a set of intelligent agents, and the development of a proof-of-concept implementation to demonstrate the effectiveness of the system. Intelligent agents can reduce the cognitive load imposed upon an operator by de-emphasizing those aspects of a display that can be inferred as less-important to the mission goals. Similarly, an intelligent agent can draw the operator’s attention to high-priority events or situations. ARID agents will be based upon ontological reasoning and feedback learning mechanisms to deliver a significant improvement over the simplistic rules-based systems that represent the current state of the art.

* information listed above is at the time of submission.

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