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Role Recognition in Massively Multiplayer Online Games

Electronic Theses and Dissertations

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abstract
In massively multiplayer online games, players have developed ways to organize themselves into roles so that they can work together to overcome the obstacles encountered in the game. This thesis explores the idea of these socially created roles, describes methods for characterizing roles in video games and elsewhere, and presents an approach to role recognition. The results presented here demonstrate that augmented Markov models can be used to achieve accurate and efficient role recognition in massively multiplayer online games.
subject
Computer Science
Artificial Intelligence
Augmented Markov Models
Video Games
Role Recognition
contributor
White, Dustin (author)
Thomas, Stan J. (committee chair)
Pauca, Victor Pául (committee member)
date
2009-06-12T19:35:57Z (accessioned)
2010-06-18T18:59:36Z (accessioned)
2009-06-12T19:35:57Z (available)
2010-06-18T18:59:36Z (available)
2009-06-12T19:35:57Z (issued)
degree
Computer Science (discipline)
identifier
http://hdl.handle.net/10339/14869 (uri)
language
en_US (iso)
publisher
Wake Forest University
rights
Release the entire work immediately for access worldwide. (accessRights)
title
Role Recognition in Massively Multiplayer Online Games
type
Thesis

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