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 wriiten a number of articles for TheConversation
http://bit.ly/1yWlOkE
How to teach Deep Blue to play poker and deliver groceries
http://bit.ly/1DXGeZD

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

Random Blog Post

Examination Timetabling: Carter Dataset

Publication(s)

A hyper-heuristic approach to sequencing by hybridization of DNA sequences
http://bit.ly/1mlNjL6
An Iterated Local Search with Multiple Perturbation Operators and Time Varying Perturbation Steength for the Aircraft Landing Problem
http://bit.ly/1KJ1818
Academic Timetabling: Linking Research and Practice
http://bit.ly/eroN3m
Automatic Design of Hyper-heuristic Framework with Gene Expression Programming for Combinatorial Optimization problems
http://bit.ly/1L6OJ8g

Graham Kendall: Details of Requested Publication


Citation

Kendall, G and Smith, C The evolution of blackjack strategies. In Proceedings of the The IEEE 2003 Congress on Evolutionary Computation (CEC2003), pages 2474-2481, Canberra, Australia, 2003.


Abstract

In this paper we investigate the evolution of a blackjack player. We utilise three neural networks (one for splitting, one for doubling down and one for standing/hitting) to evolve blackjack strategies. Initially a pool of randomly generated players play 1000 hands of blackjack. An evolutionary strategy is used to mutate the best networks (with the worst networks being killed). We compare the best evolved strategies to other well-known strategies and show that we can beat the play of an average casino player. We also show that we are able to learn parts of Thorpe’s Basic Strategy.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1109/CEC.2003.1299399 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{ks2003, author = {G. Kendall and C. Smith},
title = {The evolution of blackjack strategies},
booktitle = {Proceedings of the The IEEE 2003 Congress on Evolutionary Computation (CEC2003)},
year = {2003},
volume = {4},
pages = {2474--2481},
address = {Canberra, Australia},
month = {Dec 8 - 12},
abstract = {In this paper we investigate the evolution of a blackjack player. We utilise three neural networks (one for splitting, one for doubling down and one for standing/hitting) to evolve blackjack strategies. Initially a pool of randomly generated players play 1000 hands of blackjack. An evolutionary strategy is used to mutate the best networks (with the worst networks being killed). We compare the best evolved strategies to other well-known strategies and show that we can beat the play of an average casino player. We also show that we are able to learn parts of Thorpe’s Basic Strategy.},
comment = {IEEE Catalog Number: 03TH8674, ISBN: 0-7803-7804-0},
doi = {10.1109/CEC.2003.1299399},
keywords = {games, blackjack, evolution, evolution strategy, evolution strategies, artificail neural networks},
timestamp = {2007.03.29},
webpdf = {http://www.graham-kendall.com/papers/ks2003.pdf} }