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Evolving a Fuzzy Goal-Driven Strategy for the Game of Geister: An Exercise in Teaching Computational Intelligence

By Andrew R. Buck, Tanvi Banerjee and James M. Keller

Abstract

This paper presents an approach to designing a strategy for the game of Geister using the three main research areas of computational intelligence. We use a goal-based fuzzy inference system to evaluate the utility of possible actions and a neural network to estimate unobservable features (the true natures of the opponent ghosts). Finally, we develop a coevolutionary algorithm to learn the parameters of the strategy. The resulting autonomous gameplay agent was entered in a global competition sponsored by the IEEE Computational Intelligence Society and finished second among eight participating teams

Topics: Bioinformatics, Communication, Communication Technology and New Media, Computer Sciences, Databases and Information Systems, Life Sciences, OS and Networks, Physical Sciences and Mathematics, Science and Technology Studies, Social and Behavioral Sciences
Publisher: SelectedWorks
Year: 2017
OAI identifier: oai:works.bepress.com:tanvi-banerjee-10018
Provided by: CORE
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