KEYWORDS: Intelligence systems, Systems modeling, Environmental sensing, Detection and tracking algorithms, Data modeling, Diagnostics, Cognitive modeling, Decision support systems, Data mining, Evolutionary algorithms
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.
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