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Proceedings Paper

On deception detection in multi-agent systems and deception intent
Author(s): Eugene Santos Jr.; Deqing Li; Xiuqing Yuan
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Paper Abstract

Deception detection plays an important role in the military decision-making process, but detecting deception is a challenging task. The deception planning process involves a number of human factors. It is intent-driven where intentions are usually hidden or not easily observable. As a result, in order to detect deception, any adversary model must have the capability to capture the adversary's intent. This paper discusses deception detection in multi-agent systems and in adversary modeling. We examined psychological and cognitive science research on deception and implemented various theories of deception within our approach. First, in multi-agent expert systems, one detection method uses correlations between agents to predict reasonable opinions/responses of other agents (Santos & Johnson, 2004). We further explore this idea and present studies that show the impact of different factors on detection success rate. Second, from adversary modeling, our detection method focuses on inferring adversary intent. By combining deception "branches" with intent inference models, we can estimate an adversary's deceptive activities and at the same time enhance intent inference. Two major kinds of deceptions are developed in this approach in different fashions. Simulative deception attempts to find inconsistency in observables, while dissimulative deception emphasizes the inference of enemy intentions.

Paper Details

Date Published: 11 April 2008
PDF: 11 pages
Proc. SPIE 6965, Modeling and Simulation for Military Operations III, 696502 (11 April 2008); doi: 10.1117/12.777639
Show Author Affiliations
Eugene Santos Jr., Dartmouth College (United States)
Deqing Li, Dartmouth College (United States)
Xiuqing Yuan, Dartmouth College (United States)

Published in SPIE Proceedings Vol. 6965:
Modeling and Simulation for Military Operations III
Dawn A. Trevisani, Editor(s)

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