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Stochastic Opponent Modeling Agents: A Case Study with Hezbollah.

Authors :
Liu, Huan
Salerno, John J.
Young, Michael J.
Mannes, Aaron
Michael, Mary
Pate, Amy
Sliva, Amy
Subrahmanian, V.S.
Wilkenfeld, Jonathan
Source :
Social Computing, Behavioral Modeling & Prediction; 2008, p37-45, 9p
Publication Year :
2008

Abstract

Stochastic Opponent Modeling Agents (SOMA) have been proposed as a paradigm for reasoning about cultural groups, terror groups, and other socioeconomic- political-military organizations worldwide. In this paper, we describe a case study that shows how SOMA was used to model the behavior of the terrorist organization, Hezbollah. Our team, consisting of a mix of computer scientists, policy experts, and political scientists, were able to understand new facts about Hezbollah of which even seasoned Hezbollah experts may not have been aware. This paper briefly overviews SOMA rules, explains how more than 14,000 SOMA rules for Hezbollah were automatically derived, and then describes a few key findings about Hezbollah, enabled by this framework. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISBNs :
9780387776712
Database :
Supplemental Index
Journal :
Social Computing, Behavioral Modeling & Prediction
Publication Type :
Book
Accession number :
33759631
Full Text :
https://doi.org/10.1007/978-0-387-77672-9_6