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Algorithmic Tools for Adversarial Games

Award Information

Agency:
Department of Defense
Branch:
Air Force
Award ID:
73389
Program Year/Program:
2005 / STTR
Agency Tracking Number:
F054-017-0278
Solicitation Year:
N/A
Solicitation Topic Code:
N/A
Solicitation Number:
N/A
Small Business Information
Securboration Inc
1050 W NASA Blvd Suite 155 Melbourne, FL 32901-
View profile »
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No
 
Phase 1
Fiscal Year: 2005
Title: Algorithmic Tools for Adversarial Games
Agency / Branch: DOD / USAF
Contract: FA9550-05-C-0110
Award Amount: $100,000.00
 

Abstract:

Securboration, working with University of Connecticut researchers Dr. Eugene Santos Jr. is pleased to propose the Dynamic Adversarial Gaming Algorithm (DAGA). DAGA will focus on expanding adversarial gaming algorithms to support an agent based dynamic adversarial environment. DAGA will provide the following innovation to the adversarial gaming domain. 1) a natural mechanism to dynamically control the game based on current observables. 2) Support the interaction between agents through Web Ontology Language (OWL) based Common Operating Ontology. 3) Provided the ability for agent to split into multiple sub-agents as the population being represented diverges. 4) The use of Episodic learning to effect the group's behavior based on its experience over a period of time. Each of the listed innovation are required to support asymmetric adversarial games that represent the interaction between blue forces, red forces, and their interaction to influence green forces. The overriding goal of the DAGA service is to make accurate predication centered on a given actions ability to influence a "community of interest" to achieve a desired effect. The use of Bayesian Knowledge Fragments leverages the prediction strength of Bayesian base algorithms, along with the ability to account for prior knowledge in the prediction. In addition Bayesian Knowledge Fragments avoid the computationally cost and complexity of developing probability table typically associated with Bayesian approaches.

Principal Investigator:

Lee Krause
President
3215919836
lkrause@securboration.com

Business Contact:

Lynn Lehman
VP Operation
9192443946
llehman@securboration.com
Small Business Information at Submission:

Securboration, Inc.
695 Senderling Dr Indialantic, FL 32903

EIN/Tax ID: 593729686
DUNS: N/A
Number of Employees:
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No
Research Institution Information:
UNIV. OF CONNECTICUT
438 Whitney Road Ext, Unit 113
Storrs, CT 06269
Contact: Carol Welt
Contact Phone: (860) 486-8704
RI Type: Nonprofit college or university