Graham Kendall
Various Images

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
If you are interested in hyper-heuristics, take a look at my publications in this area
http://bit.ly/efxLGg

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Knight’s Tour

Publication(s)

Evidence and belief in regulatory decisions Incorporating expected utility into decision modelling
RATE_LIMIT_EXCEEDED
A Game Theoretic Approach for Taxi Scheduling Problem with Street Hailing
RATE_LIMIT_EXCEEDED
On Nie-Tan operator and type-reduction of interval type-2 fuzzy sets
RATE_LIMIT_EXCEEDED
A scheme for determining vehicle routes based on Arc-based service network design
RATE_LIMIT_EXCEEDED

Graham Kendall: Details of Requested Publication


Citation

Kendall, G and Willdig, M An Investigation of an Adaptive Poker player. In Proceedings of the 14th Australian Joint Conference on Artificial Intelligence (AI'01), pages 217-229, Springer-Verlag, Adelaide, Australia, 10-14 December, Lecture Notes in Artificial Intelligence 2256, 2001.

The DOI link gives the page numbers as 217-229, the PDF show the page numbers as 189-200. We assume the DOI citation is correct (i.e. pages 217-229)


Abstract

Other work has shown that adaptive learning can be highly successful in developing programs which are able to play games at a level similar to human players and, in some cases, exceed the ability of a vast majority of human players. This study uses poker to investigate how adaptation can be used in games of imperfect information. An internal learning value is manipulated which allows a poker playing agent to develop its playing strategy over time. The results suggest that the agent is able to learn how to play poker, initially losing, before winning as the players strategy becomes more developed. The evolved player performs well against opponents with different playing styles. Some limitations of previous work are overcome, such as deal rotation to remove the bias introduced by one player always being the last to act. This work provides encouragement that this is an area worth exploring more fully in our future work.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1007/3-540-45656-2_17 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

The URL for additional information is http://dx.doi.org/10.1007/3-540-45656-2

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{kw2001, author = {G. Kendall and M. Willdig},
title = {An Investigation of an Adaptive Poker player},
booktitle = {Proceedings of the 14th Australian Joint Conference on Artificial Intelligence (AI'01)},
year = {2001},
editor = {M. Stumptner and D. Corbett and M. Brooks},
volume = {2256},
series = {Lecture Notes in Artificial Intelligence},
pages = {217-229},
address = {Adelaide, Australia, 10-14 December},
publisher = {Springer-Verlag},
note = {The DOI link gives the page numbers as 217-229, the PDF show the page numbers as 189-200. We assume the DOI citation is correct (i.e. pages 217-229)},
abstract = {Other work has shown that adaptive learning can be highly successful in developing programs which are able to play games at a level similar to human players and, in some cases, exceed the ability of a vast majority of human players. This study uses poker to investigate how adaptation can be used in games of imperfect information. An internal learning value is manipulated which allows a poker playing agent to develop its playing strategy over time. The results suggest that the agent is able to learn how to play poker, initially losing, before winning as the players strategy becomes more developed. The evolved player performs well against opponents with different playing styles. Some limitations of previous work are overcome, such as deal rotation to remove the bias introduced by one player always being the last to act. This work provides encouragement that this is an area worth exploring more fully in our future work.},
comment = {ISBN 2-540-42960-3},
doi = {10.1007/3-540-45656-2_17},
keywords = {poker, games, learning},
timestamp = {2007.03.29},
url = {http://dx.doi.org/10.1007/3-540-45656-2},
webpdf = {http://www.graham-kendall.com/papers/kw2001.pdf} }