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Adaptive and Embeddable Agents for Real-Time Cognitive Readiness and Performance

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-11-M-0103
Agency Tracking Number: O102-CR9-1024
Amount: $99,868.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: OSD10-CR9
Solicitation Number: 2010.2
Timeline
Solicitation Year: 2010
Award Year: 2011
Award Start Date (Proposal Award Date): 2010-12-14
Award End Date (Contract End Date): N/A
Small Business Information
3 Innovation Way Suite 100
Newark, DE -
United States
DUNS: 077990047
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Jijun Wang
 Sr. Software Scientist
 (302) 894-8055
 jw@quantumleap.us
Business Contact
 Frank Abbott
Title: VP of Administration&Finace, CFO
Phone: (302) 894-8045
Email: fta@quantumleap.us
Research Institution
 Stub
Abstract

To meet the ever-increasing challenges imposed by next generation weapon systems and continued reduced manning efforts, Quantum Leap Innovations, Inc. will collaborate with the Institute for Simulation and Training (IST) at the University of Central Florida (UCF) to develop adaptive, embeddable, and configurable agents for real-time cognitive readiness and performance assessment. The objective of this SBIR is to develop and operationally test adaptive and embeddable agents that can be seamlessly integrated into existing military systems with minimal changes and enable warfighters to work effectively and safely in highly complex and uncertain environments. The agents will utilize a Bayesian network model to learn the probabilistic relationships between cognitive states of a human operator and his/her performance from neural, cognitive and behavioral data streams. Additionally, the Bayesian network model is able to adapt to different military systems based on the profile of users and task environments. In Phase I of this SBIR, we will demonstrate the benefits of configurable agent architecture and interfaces in the Mixed Initiative Experimental (MIX) Testbed a distributed training testbed for human-robot teams. We will further evaluate the feasibility of the Bayesian network modeling for single and multi-modal cognitive state assessment using data collected by UCF-IST.

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

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