Evolving a Fuzzy Goal-Driven Strategy for the Game of Geister: An Exercise in Teaching Computational Intelligence
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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.
Buck, A. R.,
& Keller, J. M.
(2014). Evolving a Fuzzy Goal-Driven Strategy for the Game of Geister: An Exercise in Teaching Computational Intelligence. 2014 IEEE Congress on Evolutionary Computation, 28-35.