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dc.contributor.authorBlackman, Todd
dc.contributor.authorAgah, Arvin
dc.date.accessioned2016-01-29T16:38:23Z
dc.date.available2016-01-29T16:38:23Z
dc.date.issued2011
dc.identifier.citationBlackman, Todd, and Arvin Agah. "A Multi-Agent Approach to the Game of Go Using Genetic Algorithms." Journal of Intelligent Systems 18.1-2 (2011): n. pag. http://dx.doi.org/10.1515/JISYS.2009.18.1-2.143.en_US
dc.identifier.urihttp://hdl.handle.net/1808/19816
dc.descriptionThis is the published version. Copyright De Gruyteren_US
dc.description.abstractMany researchers have written or attempted to write programs that play the ancient Chinese board game called Go. Although some programs play the game quite well compared with beginners, few play extremely well, and none of the best programs rely on soft computing artificial intelligence techniques like genetic algorithms or neural networks. This paper explores the advantages and possibilities of using genetic algorithms to evolve a multiagent Go player. We show that although individual agents may play poorly, collectively the agents working together play the game significantly better.en_US
dc.publisherDe Gruyteren_US
dc.subjectGame of Goen_US
dc.subjectGenetic algorithmsen_US
dc.subjectMulti-agent systemsen_US
dc.titleA Multi-Agent Approach to the Game of Go Using Genetic Algorithmsen_US
dc.typeArticle
kusw.kuauthorAgah, Arvin
kusw.kudepartmentEngineering Administrationen_US
dc.identifier.doi10.1515/JISYS.2009.18.1-2.143
kusw.oaversionScholarly/refereed, publisher version
kusw.oapolicyThis item meets KU Open Access policy criteria.
dc.rights.accessrightsopenAccess


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