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

Does AI have a place in the board room?
http://bit.ly/1DXreuW
I am involved with a spin out company that specialises in Strategic Resource Planning
http://bit.ly/eTPZO2

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

Random Blog Post

Conjuring Trick

Publication(s)

Learning with imperfections - a multi-agent neural-genetic trading system with differing levels of social learning
http://bit.ly/hBQypU
A Variable Neighborhood Descent Search Algorithm for Delay-Constrained Least-Cost Multicast Routing
http://bit.ly/h1puUB
Does money matter in inflation forecasting?
http://bit.ly/fRebpX
Evidence and belief in regulatory decisions Ė Incorporating expected utility into decision modelling
http://bit.ly/1iaJTKT

Graham Kendall: Details of Requested Publication


Citation

Al-Khateeb, B and Kendall, G Introducing Individual and Social Learning Into Evolutionary Checkers. IEEE Transactions on Computational Intelligence and AI in Games, 4 (4): 258-269, 2012.


Abstract

In recent years, much research attention has been paid to evolving self-learning game players. Fogelís Blondie24 is just one demonstration of a real success in this field and it has inspired many other scientists. In this paper, evolutionary neural networks, evolved via an evolution strategy, are employed to evolve game-playing strategies for the game of Checkers. In addition, we introduce an individual and social learning mechanism into the learning phase of this evolutionary Checkers system. The best player obtained is tested against an implementation of an evolutionary Checkers program, and also against a player, which has been evolved within a round robin tournament. The results are promising and demonstrate that using individual and social learning enhances the learning process of the evolutionary Checkers system and produces a superior player compared to what was previously possible.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1109/TCIAIG.2012.2209424 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


Journal Rankings


ISI Web of Knowledge Journal Citation Reports

The Web of Knowledge Journal Citation Reports (often known as ISI Impact Factors) help measure how often an article is cited. You can get an introduction to Journal Citation Reports here. Below I have provided the ISI impact factor for the jourrnal in which this article was published. For complete information I have shown the ISI ranking over a number of years, with the latest ranking highlighted.

2014 (1.481), 2013 (1.167), 2012 (1.694), 2011 (1.617)

URL

This pubication does not have a URL associated with it.

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

@ARTICLE{ak2012, author = {B. Al-Khateeb and G. Kendall},
title = {Introducing Individual and Social Learning Into Evolutionary Checkers},
journal = {IEEE Transactions on Computational Intelligence and AI in Games},
year = {2012},
volume = {4},
pages = {258--269},
number = {4},
abstract = {In recent years, much research attention has been paid to evolving self-learning game players. Fogelís Blondie24 is just one demonstration of a real success in this field and it has inspired many other scientists. In this paper, evolutionary neural networks, evolved via an evolution strategy, are employed to evolve game-playing strategies for the game of Checkers. In addition, we introduce an individual and social learning mechanism into the learning phase of this evolutionary Checkers system. The best player obtained is tested against an implementation of an evolutionary Checkers program, and also against a player, which has been evolved within a round robin tournament. The results are promising and demonstrate that using individual and social learning enhances the learning process of the evolutionary Checkers system and produces a superior player compared to what was previously possible.},
doi = {10.1109/TCIAIG.2012.2209424},
issn = {1943-068X},
keywords = {Checkers, Evolutionary Algorithm, Blondie24, Games},
owner = {gxk},
timestamp = {2012.12.17},
webpdf = {http://www.graham-kendall.com/papers/ak2012.pdf} }