Deception Detection in Expert Source Information Through Bayesian Knowledge-Bases
Abstract
Our goal in this effort was to automatically detect deception by an individual or expert who is contributing to an information knowledge-base consisting of multiple experts. 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, through data mining techniques, and so forth. As a decision maker's sources become more numerous, detecting deceptive information from these sources becomes vital to making a correct, or at least more informed, decision. This applies to unintentional misinformation as well as intentional disinformation. We have developed definitions for deception intent and potential mechanisms for capture such intentions and how to carry them out. We have also defined a number of concepts such as deception attempt, the deception core, effective deception and successful deception. A deception attempt occurs when the opinions returned to a decision maker by an expert agent are not those calculated by that expert agent with the given observations but are substituted to influence the decision maker's actions. The deception core refers to those opinions which are manipulated to form a deception attempt. An effective deception is a deception attempt which succeeds in altering the actions of the decision maker, though not necessarily to the actions desired by the deceptive expert. Finally, a successful deception is an effective deception in which the alternate actions which the decision maker chooses are those desired by the deceptive expert. We have focused on employing models of deception and deception detection from the fields of psychology, cognitive science and artificial intelligence and have implemented deception detection algorithms using probabilistic, intelligent, multiagent systems. We have also conducted numerous experiments to explore and validate our approach.
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 04, 2008
- Accession Number
- ADA531719
Entities
People
- Eugene Santos