Graham Kendall
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Professor Graham Kendall

Professor Graham Kendall is the Provost and CEO of The University of Nottingham Malaysia Campus (UNMC). He is also a Pro-Vice Chancellor of the University of Nottingham.

He is a Director of MyResearch Sdn Bhd, Crops for the Future Sdn Bhd. and Nottingham Green Technologies Sdn Bhd. He is a Fellow of the British Computer Society (FBCS) and a Fellow of the Operational Research Society (FORS).

He has published over 230 peer reviewed papers. He is an Associate Editor of 10 journals and the Editor-in-Chief of the IEEE Transactions of Computational Intelligence and AI in Games.

News

I have published a number of papers on Cutting and Packing
http://bit.ly/dQPw7T
I have published some papers on timetabling.
http://bit.ly/hSGAhZ

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

PATAT 2010: Googlemaps and Multimaps

Publication(s)

Studying the Effect that a Linear Transformation has on the Time-Series Prediction Ability of an Evolutionary Neural Network
http://bit.ly/eyLaq2
A Multi-objective Hyper-heuristic based on Choice Function
http://bit.ly/1f8GQgU
A path-oriented encoding evolutionary algorithm for network coding resource minimization
http://bit.ly/1t0cd2K
A Dynamic Multiarmed Bandit-Gene Expression Programming Hyper-Heuristic for Combinatorial Optimization Problems
http://bit.ly/1AfTYKx

Graham Kendall: Details of Requested Publication


Citation

Kendall, G; Yaakob, R and Hingston, P An Investigation of an Evolutionary Approach to the Opening of Go. In Proceedings of the 2004 IEEE Congress on Evolutionary Computation (CEC'04), pages 2052-2059, Portland, Oregon, 2004.


Abstract

The game of Go can be divided into three stages; the opening, the middle, and the end game. In this paper, evolutionary neural networks, evolved via an evolutionary strategy, are used to develop opening game playing strategies for the game. Go is typically played on one of three different board sizes, i.e., 9x9, 13x13 and 19x19. A 19x19 board is the standard size for tournament play but 9x9 and 13x13 boards are usually used by less-experienced players or for faster games. This paper focuses on the opening, using a 13x13 board. A feed forward neural network player is played against a static player (Gondo), for the first 30 moves. Then Gondo takes the part of both players to play out the remainder of the game. Two experiments are presented which indicate that learning is taking place.


pdf

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doi

The doi for this publication is 10.1109/CEC.2004.1331149 You can link directly to the original paper, via the doi, from here

What is a doi?: A doi (Document Object Identifier) is a unique identifier for sicientific papers (and occasionally other material). This provides direct access to the location where the original article is published using the URL http://dx.doi/org/xxxx (replacing xxx with the doi). See http://dx.doi.org/ for more information



URL

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Bibtex

@INPROCEEDINGS{kyh2004, author = {G. Kendall and R. Yaakob and P. Hingston},
title = {An Investigation of an Evolutionary Approach to the Opening of Go},
booktitle = {Proceedings of the 2004 IEEE Congress on Evolutionary Computation (CEC'04)},
year = {2004},
pages = {2052--2059},
address = {Portland, Oregon},
month = {20-23 June},
abstract = {The game of Go can be divided into three stages; the opening, the middle, and the end game. In this paper, evolutionary neural networks, evolved via an evolutionary strategy, are used to develop opening game playing strategies for the game. Go is typically played on one of three different board sizes, i.e., 9x9, 13x13 and 19x19. A 19x19 board is the standard size for tournament play but 9x9 and 13x13 boards are usually used by less-experienced players or for faster games. This paper focuses on the opening, using a 13x13 board. A feed forward neural network player is played against a static player (Gondo), for the first 30 moves. Then Gondo takes the part of both players to play out the remainder of the game. Two experiments are presented which indicate that learning is taking place.},
comment = {ISBN 0-7803-8515-2},
doi = {10.1109/CEC.2004.1331149},
keywords = {Go, evolution, artificial neural networks, evolution strategy},
timestamp = {2007.03.29},
webpdf = {http://www.graham-kendall.com/papers/kyh2004.pdf} }