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Proceedings Paper

Game theoretic behavior features change prediction in hostile environments
Author(s): Mo Wei; Erik Blasch; Genshe Chen; Jose B. Cruz; Leonard Haynes; Martin Kruger; Irma Sityar
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Paper Abstract

Prediction of adversarial course of actions (COA) is critical to many applications including: crime prediction, Unmanned Aerial Vehicle (UAV) threat prediction, and terrorism attack prevention. Researchers have shown that integrating behavior features (or preferences/patterns/modes) into prediction systems, which utilize random process theory and likelihood estimation calculations, can improve prediction accuracy. However, these calculations currently assume behavior features that are static and will not change during a long time horizon, which make such models difficult to adapt to adversary behavior feature changes. This paper provides an approach for dynamically predicting changes of behavior features utilizing the tenets of game theory. An example scenario and extensive simulations illustrate the feature prediction capability of this model.

Paper Details

Date Published: 7 May 2007
PDF: 9 pages
Proc. SPIE 6567, Signal Processing, Sensor Fusion, and Target Recognition XVI, 656713 (7 May 2007); doi: 10.1117/12.719016
Show Author Affiliations
Mo Wei, Intelligent Automation Inc. (United States)
Erik Blasch, Air Force Research Lab. (United States)
Genshe Chen, Intelligent Automation Inc. (United States)
Jose B. Cruz, The Ohio State Univ. (United States)
Leonard Haynes, Intelligent Automation, Inc. (United States)
Martin Kruger, Office of Naval Research (United States)
Irma Sityar, Alion Science and Technology (United States)

Published in SPIE Proceedings Vol. 6567:
Signal Processing, Sensor Fusion, and Target Recognition XVI
Ivan Kadar, Editor(s)

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