|Institution:||Wake Forest University|
|Full text PDF:||http://hdl.handle.net/10339/14869|
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.