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

The hunt for MH370
http://bit.ly/1DXRLbu
Does AI have a place in the board room?
http://bit.ly/1DXreuW

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

Random Blog Post

AlphaGo: Computers and Game Playing: A Very Timely Lecture

Publication(s)

A Hybrid Differential Evolution Algorithm - Game Theory for the Berth Allocation Problem
http://bit.ly/1OjS0k9
Heuristic Space Diversity Management in a Meta-Hyper-Heuristic Framework
http://bit.ly/1uuQW45
A Hybrid Differential Evolution Algorithm - Game Theory for the Berth Allocation Problem
http://bit.ly/1OjS0k9
The Application of a Dendritic Cell Algorithm to a Robotic Classifier
http://bit.ly/hTMQ5K

Graham Kendall: Details of Requested Publication


Citation

Al-Khateeb, B and Kendall, G The Importance of Look-Ahead Depth in Evolutionary Checkers. In Proceedings of the 2011 Congress of Evolutionary Computation, 2011 (CEC 2011), pages 2252-2258, 2011.


Abstract

Intuitively it would seem to be the case that any learning algorithm would perform better if it was allowed to search deeper in the game tree. However, there has been some discussion as to whether the evaluation function or the depth of the search is the main contributory factor in the performance of the player. There has been some evidence suggesting that lookahead (i.e. depth of search) is particularly important. In this work we provide a rigorous set of experiments, which support this view. We believe this is the first time such an intensive study has been carried out for evolutionary checkers. Our experiments show that increasing the depth of a look-ahead has significant improvements to the performance of the checkers program and has a significant effect on its learning abilities.


pdf

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doi

The doi for this publication is 10.1109/CEC.2011.5949894 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{ak2011a, author = {B. Al-Khateeb and G. Kendall},
title = {The Importance of Look-Ahead Depth in Evolutionary Checkers},
booktitle = {Proceedings of the 2011 Congress of Evolutionary Computation, 2011 (CEC 2011)},
year = {2011},
pages = {2252--2258},
month = {5-8 June 2011},
organization = {New Orleans, USA},
abstract = {Intuitively it would seem to be the case that any learning algorithm would perform better if it was allowed to search deeper in the game tree. However, there has been some discussion as to whether the evaluation function or the depth of the search is the main contributory factor in the performance of the player. There has been some evidence suggesting that lookahead (i.e. depth of search) is particularly important. In this work we provide a rigorous set of experiments, which support this view. We believe this is the first time such an intensive study has been carried out for evolutionary checkers. Our experiments show that increasing the depth of a look-ahead has significant improvements to the performance of the checkers program and has a significant effect on its learning abilities.},
doi = {10.1109/CEC.2011.5949894},
keywords = {Checkers, Blondie24, Draughts, Games, Computational Intelligence, Neural Networks, evolutionary computation},
owner = {gxk},
timestamp = {2010.12.11},
webpdf = {http://www.graham-kendall.com/papers/ak2011a.pdf} }