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 language | English |
---|---|
Title of host publication | 13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014 |
Publisher | International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) |
Pages | 1387-1388 |
Number of pages | 2 |
ISBN (Electronic) | 9781634391313 |
State | Published - 2014 |
Event | 13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014 - Paris, France Duration: 5 May 2014 → 9 May 2014 |
Publication series
Name | 13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014 |
---|---|
Volume | 2 |
Conference
Conference | 13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014 |
---|---|
Country/Territory | France |
City | Paris |
Period | 5/05/14 → 9/05/14 |
Bibliographical note
Publisher Copyright:Copyright © 2014, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.
Keywords
- Deception detection
- Discussions
- Human modeling