{"id":{"repo_id":"wfu","oai_identifier":"oai:wakespace.lib.wfu.edu:10339/14869"},"canonical_url":"https://search.dev.ndltd.org/etd/wfu/oai:wakespace.lib.wfu.edu:10339/14869","repository":{"repo_id":"wfu","name":"Wake Forest University","base_url":"https://wakespace.lib.wfu.edu/oai/request"},"display":{"title":"Role Recognition in Massively Multiplayer Online Games","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.","abstract_html":"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.","abstract_has_math":false,"creators":["White, Dustin"],"institution":"Wake Forest University","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2009,"date_issued":"2009-06-12T19:35:57Z","date_published":"2009-06-12T19:35:57Z","updated_at":"2026-07-27T22:01:07Z","subjects":["Computer Science"],"languages":["en_US"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10339/14869","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["White, Dustin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2009-06-12T19:35:57Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2009-06-12T19:35:57Z"]},{"key":"dc:date.issued","label":"Date","values":["2009-06-12T19:35:57Z"]},{"key":"dc:publisher","label":"Institution","values":["Wake Forest University"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Computer Science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_US"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10339/14869"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["Role Recognition in Massively Multiplayer Online Games"]}]}],"canonical_facts":{"dc:creator":["White, Dustin"],"dc:date.accessioned":["2009-06-12T19:35:57Z"],"dc:date.available":["2009-06-12T19:35:57Z"],"dc:date.issued":["2009-06-12T19:35:57Z"],"dc:description.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."],"dc:identifier.uri":["http://hdl.handle.net/10339/14869"],"dc:language.iso":["en_US"],"dc:publisher":["Wake Forest University"],"dc:subject":["Computer Science"],"dc:title":["Role Recognition in Massively Multiplayer Online Games"],"dc:type":["Thesis"]},"updated_at":"2026-07-27T22:01:07Z"}