Improving Team Cognitive Readiness through the Multi-Agent System for Targeting Team Mental Models (MAST-TMM)

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-13-C-0059
Agency Tracking Number: O2-1291
Amount: $746,509.00
Phase: Phase II
Program: SBIR
Awards Year: 2013
Solicitation Year: 2010
Solicitation Topic Code: OSD10-CR7
Solicitation Number: 2010.2
Small Business Information
SA Technologies, Inc.
3750 Palladian Village Drive, Building 600, Marietta, GA, -
DUNS: 179321302
HUBZone Owned: N
Woman Owned: Y
Socially and Economically Disadvantaged: N
Principal Investigator
 Laura Strater
 Principal Research Associ
 (972) 636-8312
 laura@satechnologies.com
Business Contact
 Ronda Butler
Title: Contracts Administrator I
Phone: (770) 565-9859
Email: ronda.butler@satechnologies.com
Research Institution
N/A
Abstract
Navy command and control (C2) personnel must make decisions about events and activity they are not able to directly perceive. They must interpret information represented on an interface or over a communication device into a mental picture of the relevant environment - a mental model of the current situation. Sustaining high levels of team performance in this environment is difficult. A tool to predict and improve team cognitive readiness in this domain could pay dividends in improving performance in a demanding environment. SA Technologies proposes to develop a comprehensive, theoretically derived multi-agent system for targeting Team Mental Models (MAST-TMM). The proposed research will develop a cognitive readiness assessment tool that provides a comprehensive assessment of TMM to predict team performance in a variety of operational environments, and identify areas of team convergence and divergence that allows prescriptive action. We will develop a cognitive readiness assessment system that provides a comprehensive assessment of TMM by integrating measures of both explicit and tacit knowledge structures, team member skills and abilities, and team member attitudes, combining these elements using fuzzy cognitive modeling in a multi-agent method, and including periodic model updating using automated communication analysis.

* information listed above is at the time of submission.

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