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

How are football fixtures worked out?
http://bit.ly/1z0oTAH
I have wriiten a number of articles for TheConversation
http://bit.ly/1yWlOkE

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

What is Operations Research?

Publication(s)

Scheduling English Football Fixtures: Consideration of Two Conflicting Objectives
http://bit.ly/1hWKuwV
A Particle Swarm Optimisation Approach in the Construction of Optimal Risky Portfolios
http://bit.ly/f6EKmF
An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems
http://bit.ly/29btjbV
Using Harmony Search with Multiple Pitch Adjustment Operators for the Portfolio Selection Problem
http://bit.ly/1okj5eC

Graham Kendall: Details of Requested Publication


Citation

Al-Khateeb, B and Kendall, G Introducing a round robin tournament into Blondie24. In Proceedings of Computational Intelligence and Games, 2009 (CIG 2009), pages 112-116, 2009.


Abstract

Evolving self-learning players has attracted a lot of research attention in recent years. Fogel's Blondie24 represents one of the successes in this field and a strong motivating factor for other scientists. In this paper evolutionary neural networks, evolved via an evolution strategy, are utilised to evolve game playing strategies for the game of checkers by introducing a league structure into the learning phase of a system based on Blondie24. We believe that this helps eliminate some of the randomness in the evolution. Thirty feed forward neural network players are played against each other, using a round robin tournament structure, for 150 generations and the best player obtained is tested against a reimplementation of Blondie24. We also test the best player against an online program, as well as two other strong programs. The results obtained are promising.


pdf

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doi

The doi for this publication is 10.1109/CIG.2009.5286487 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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The URL is only provided if there is additional information that might be useful. For example, where the entry is a book chapter, the URL might link to the book itself.


Bibtex

@INPROCEEDINGS{ak2009, author = {B. Al-Khateeb and G. Kendall},
title = {Introducing a round robin tournament into Blondie24},
booktitle = {Proceedings of Computational Intelligence and Games, 2009 (CIG 2009)},
year = {2009},
pages = {112--116},
month = {7-10 Sept. 2009},
organization = {Milano, Italy},
abstract = {Evolving self-learning players has attracted a lot of research attention in recent years. Fogel's Blondie24 represents one of the successes in this field and a strong motivating factor for other scientists. In this paper evolutionary neural networks, evolved via an evolution strategy, are utilised to evolve game playing strategies for the game of checkers by introducing a league structure into the learning phase of a system based on Blondie24. We believe that this helps eliminate some of the randomness in the evolution. Thirty feed forward neural network players are played against each other, using a round robin tournament structure, for 150 generations and the best player obtained is tested against a reimplementation of Blondie24. We also test the best player against an online program, as well as two other strong programs. The results obtained are promising.},
doi = {10.1109/CIG.2009.5286487},
keywords = {Checkers, Blondie24, Draughts, Games, Computational Intelligence, Neural Networks, evolutionary computation},
owner = {gxk},
timestamp = {2010.12.11},
webpdf = {http://www.graham-kendall.com/papers/ak2009.pdf} }