2026-09-20T06:26:28Zhttps://wakespace.lib.wfu.edu/server/oai/request

oai:null:10339/148692026-09-02T11:56:01Zcom_10339_14934col_10339_38132

White, Dustin 2009-06-12T19:35:57Z 2010-06-18T18:59:36Z 2009-06-12T19:35:57Z 2010-06-18T18:59:36Z 2009-06-12T19:35:57Z https://wakespace.lib.wfu.edu/handle/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. en_US Wake Forest University Computer Science Artificial Intelligence Augmented Markov Models Video Games Role Recognition Role Recognition in Massively Multiplayer Online Games Thesis Release the entire work immediately for access worldwide. Thomas, Stan J. Pauca, Victor Pául Computer Science open.access