A Coevolutionary Model for The Virus Game

Author

Cowling, P.I. ; Naveed, M.H. ; Hossain, M.A.

Author_Institution

Dept. of Comput., Bradford Univ.

fYear

2006

fDate

38838

Firstpage

45

Lastpage

51

Abstract

In this paper, coevolution is used to evolve artificial neural networks (ANN) which evaluate board positions of a two player zero-sum game (the virus game). The coevolved neural networks play at a level that beats a group of strong hand-crafted AI players. We investigate the performance of coevolution starting from random initial weights and starting with weights that are tuned by gradient based adaptive learning methods (backpropagation, RPROP and iRPROP). The results of coevolutionary experiments show that pre training of the population is highly effective in this case

Keywords

backpropagation; computer games; evolutionary computation; gradient methods; neural nets; artificial neural networks; board position evaluation; coevolutionary model; gradient based adaptive learning; resilient backpropagation; two-player zero-sum game; virus game; Artificial neural networks; Backpropagation; Computer errors; Computer networks; Electronic mail; Evolution (biology); Learning systems; Neural networks; Supervised learning; Unsupervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Games, 2006 IEEE Symposium on

Conference_Location

Reno, NV

Print_ISBN

1-4244-0464-9

Type

conf

DOI

10.1109/CIG.2006.311680

Filename

4100107