NDLI: Spatial processing layer effects on the evolution of neural networks to play the game of Othello

Content Provider	IEEE Xplore Digital Library
Author	Lin, S.Y. White, J.D.
Copyright Year	2009
Description	Author affiliation: Department of Electrical Engineering, Yuan Ze University, Taoyuan 320, Taiwan (Lin, S.Y.; White, J.D.)
Abstract	Neural networks (NNs) were evolved to learn to play the zero-sum game Othello (also known as reversi) without relying on a-priori or expert knowledge. Such neural networks were able to discover game-playing strategies through co-evolution, where the neural networks just play against themselves across generations. The effect of the spatial processing layer on evolution was investigated. It was found that the evolutionary process was crucially dependent on the way in which spatial information was presented. A simple sampling pattern based on the squares attacked by a single queen in Chess resulted the networks converging to a solution in which the majority of networks, handicapped by playing Black and playing without using any look-ahead algorithm, could defeat a positional strategy using look-ahead at plydepth=4 and a piece-differential strategy using look-ahead at ply-depth=6. Improvement and convergence was observed to be accompanied by an gradual increase in the survival time of neural network strategies from less than 10 generations to about 600 generations. Surprisingly, evolved neural networks had difficulty in defeating a simple mobility strategy playing at a ply-depth=2. This work suggests that in deciding a suitable way to spatially sample a board position, it is important to consider the rules of the game.
Starting Page	646
Ending Page	651
File Size	373053
Page Count	6
File Format	PDF
ISBN	9781424429585
DOI	10.1109/CEC.2009.4983006
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-18
Publisher Place	Norway
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Law Legal factors Costs Noise reduction Genetic programming Sampling methods Automatic testing Multi-layer neural network
Content Type	Text
Resource Type	Article

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