TY - Generic
T1 - Evolving a Fuzzy Goal-Driven Strategy for the Game of Geister
T2 - 2014 IEEE Congress on Evolutionary Computation (CEC)
Y1 - 2014
A1 - Andrew Buck
A1 - Tanvi Banerjee
A1 - James Keller
KW - autonomous gameplay agent
KW - coevolutionary algorithm
KW - computational intelligence
KW - computer games
KW - evolutionary computation
KW - fuzzy goal-driven strategy
KW - Fuzzy Logic
KW - fuzzy reasoning
KW - Games
KW - German for ghosts game
KW - goal-based fuzzy inference system
KW - IEEE Computational Intelligence Society
KW - Inference algorithms
KW - multi-agent systems
KW - neural nets
KW - neural network
KW - Neural networks
KW - teaching
KW - Training
KW - unobservable feature estimation
KW - Vectors
AB - 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.
JA - 2014 IEEE Congress on Evolutionary Computation (CEC)
PB - IEEE
CY - Beijing, China
ER -