An agent for deception detection in discussion based environments

Amos Azaria, Ariella Richardson, Sarit Kraus

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Autonomous agents can be of assistance in detecting and reducing deception in computerized forums and chat-rooms. We focus on text-based environments where the deceiver is a member of a group which is holding a discussion. Deception detection methods which currently exist for such environments, heavily rely on either audio or visual information. We have developed DIG, an innovative machine learning-based autonomous agent, which joins a group of players as a regular member and assists them in catching a deceiver. We introduce "the pirate game" as a platform for deploying this agent. Our experimental study shows that although humans display difficulty detecting deception, DIG is not only capable of finding a deceptive player, it also helps increase the entire group's success.

Original languageEnglish
Title of host publication13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014
Pages1387-1388
Number of pages2
ISBN (Electronic)9781634391313
StatePublished - 2014
Externally publishedYes
Event13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014 - Paris, France
Duration: 5 May 20149 May 2014

Publication series

Name13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014
Volume2

Conference

Conference13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014
Country/TerritoryFrance
CityParis
Period5/05/149/05/14

Keywords

  • Deception detection
  • Discussions
  • Human modeling

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