%0 Conference Proceedings
%B 2014 IEEE Congress on Evolutionary Computation (CEC)
%D 2014
%T Evolving a Fuzzy Goal-Driven Strategy for the Game of Geister
%A Andrew Buck
%A Tanvi Banerjee
%A James Keller
%K autonomous gameplay agent
%K coevolutionary algorithm
%K computational intelligence
%K computer games
%K evolutionary computation
%K fuzzy goal-driven strategy
%K Fuzzy Logic
%K fuzzy reasoning
%K Games
%K German for ghosts game
%K goal-based fuzzy inference system
%K IEEE Computational Intelligence Society
%K Inference algorithms
%K multi-agent systems
%K neural nets
%K neural network
%K Neural networks
%K teaching
%K Training
%K unobservable feature estimation
%K Vectors
%X 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.
%B 2014 IEEE Congress on Evolutionary Computation (CEC)
%I IEEE
%C Beijing, China
%P 28 - 35
%8 07/2014
%G eng
%M 14600095
%R 10.1109/CEC.2014.6900568