On Deception Detection in Multiagent Systems

Abstract

Deception detection plays an important role in safely and reliably using multientity advisory models such as multiagent intelligence systems. The benevolence assumption people have based their implementations of multiagent (human and/or synthetic) systems on is rarely valid in the real world. Unfortunately, deception detection is extremely challenging. The average detection rate by humans alone is only above chance, and the skill for detection has been shown to be difficult to improve even with training. In psychological studies, deception detection is typically based on examining a person's nonverbal cues and expressions such as facial expressions, gestures, and movements. In this paper, our approach instead is focused on the agent's reasoning process. We detect deception by observing the correlations between agents, which can be used to make a reasonable prediction of the agents' reasoning processes. Our experiments demonstrate the effectiveness of this method and show the impact of different factors on detection rate. We further conduct some preliminary experiments to explore its performance at detecting both disinformation and misinformation and that of identifying more than one deceiver in the system.