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Proceedings Paper

Toward detecting deception in intelligent systems
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Paper Abstract

Contemporary decision makers often must choose a course of action using knowledge from several sources. Knowledge may be provided from many diverse sources including electronic sources such as knowledge-based diagnostic or decision support systems or through data mining techniques. As the decision maker becomes more dependent on these electronic information sources, detecting deceptive information from these sources becomes vital to making a correct, or at least more informed, decision. This applies to unintentional disinformation as well as intentional misinformation. Our ongoing research focuses on employing models of deception and deception detection from the fields of psychology and cognitive science to these systems as well as implementing deception detection algorithms for probabilistic intelligent systems. The deception detection algorithms are used to detect, classify and correct attempts at deception. Algorithms for detecting unexpected information rely upon a prediction algorithm from the collaborative filtering domain to predict agent responses in a multi-agent system.

Paper Details

Date Published: 13 August 2004
PDF: 12 pages
Proc. SPIE 5423, Enabling Technologies for Simulation Science VIII, (13 August 2004); doi: 10.1117/12.547296
Show Author Affiliations
Eugene Santos Jr., Univ. of Connecticut (United States)
Gregory Johnson Jr., Univ. of Connecticut (United States)

Published in SPIE Proceedings Vol. 5423:
Enabling Technologies for Simulation Science VIII
Dawn A. Trevisani; Alex F. Sisti, Editor(s)

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