NDLI: Observing the evolution of neural networks learning to play the game of Othello

Content Provider	IEEE Xplore Digital Library
Author	Chong, S.Y. Tan, M.K. White, J.D.
Copyright Year	1997
Abstract	A study was conducted to find out how game-playing strategies for Othello (also known as reversi) can be learned without expert knowledge. The approach used the coevolution of a fixed-architecture neural-network-based evaluation function combined with a standard minimax search algorithm. Comparisons between evolving neural networks and computer players that used deterministic strategies allowed evolution to be observed in real-time. Neural networks evolved to outperform the computer players playing at higher ply-depths, despite being handicapped by playing black and using minimax at ply-depth of two. In addition, the playing ability of the population progressed from novice, to intermediate, and then to master's level. Individual neural networks discovered various game-playing strategies, starting with positional and later mobility. These results show that neural networks can be evolved as evaluation functions, despite the general difficulties associated with this approach. Success in this case was due to a simple spatial preprocessing layer in the neural network that captured spatial information, self-adaptation of every weight and bias of the neural network, and a selection method that allowed a diverse population of neural networks to be carried forward from one generation to the next.
Starting Page	240
Ending Page	251
Page Count	12
File Size	661371
File Format	PDF
ISSN	1089778X
Volume Number	9
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-06-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Game theory Artificial intelligence Evolutionary computation Artificial neural networks Humans Minimax techniques Computer networks Intelligent networks System testing Othello coevolution evolutionary computation neural networks
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Computational Theory and Mathematics Software

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