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Adaptive Model-based Adversarial Reasoning System (AMARS) for Enhanced Synthetic Battlespaces

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8650-06-C-4401
Agency Tracking Number: F051-089-0934
Amount: $749,484.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: AF05-089
Solicitation Number: 2005.1
Timeline
Solicitation Year: 2005
Award Year: 2006
Award Start Date (Proposal Award Date): 2006-05-03
Award End Date (Contract End Date): 2008-08-03
Small Business Information
625 Mount Auburn Street
Cambridge, MA 02138
United States
DUNS: 115243701
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Paul Gonsalves
 Principal Scientist
 (617) 491-3474
 pgonsalves@cra.com
Business Contact
 Greg Zacharias
Title: President
Phone: (617) 491-3474
Email: glz@cra.com
Research Institution
N/A
Abstract

Recent military operations have demonstrated the use by adversaries of non-traditional or asymmetric military tactics to offset US military might. This issue has thus come into the forefront of national security. Rogue nations with links to trans-national terrorists have created a highly unpredictable and potential dangerous environment for US military operations. In his testimony to the US Senate, Vice Admiral Wilson, Director of the Defense Intelligence Agency (DIA) identifies several characteristics of these threats including extremism in beliefs, global in nature, non-state oriented, and highly networked and adaptive, thus making these adversaries less vulnerable to conventional military approaches. Additionally, US forces must also contend with more traditional state-based threats that are further evolving their military fighting strategies and capabilities. What are needed are solutions to assist our forces in the prosecution of operations against these diverse threat types and their atypical strategies and tactics. We propose an Adaptive Model-based Adversarial Reasoning System (AMARS) to support both training and simulation based acquisition requirements for effective responses to enemy asymmetric tactics and strategies. The system generates and adapts an adversary model that reasons on both the entity and organizational level via an evolutionary algorithm using a low-fidelity simulation environment.

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

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