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An agent for deception detection in discussion based environments

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 2014 → 9 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
Period5/05/14 → 9/05/14

Keywords

  • Deception detection
  • Discussions
  • Human modeling

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