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 published a number of papers on Cutting and Packing
http://bit.ly/dQPw7T
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

Numberphile (Statistics on Match Day)

Publication(s)

Automated tile design for self-assembly conformations
http://bit.ly/h7QYiX
Introducing a round robin tournament into Blondie24
http://bit.ly/f4DBrz
Grammatical Evolution Hyper-Heuristic for Combinatorial Optimization Problems
http://bit.ly/19D3Bqv
Investigating the Use of Local Search for Improving Meta-Hyper-Heuristic Performance
http://bit.ly/1csb3rG

Graham Kendall: Details of Requested Publication


Citation

Li, J and Kendall, G A hyper-heuristic methodology to generate adaptive strategies for games. IEEE Transactions on Computational Intelligence and AI in Games, 9 (1): 1-10, 2017.


Abstract

Hyper-heuristics have been successfully applied in solving a variety of computational search problems. In this study, we investigate a hyper-heuristic methodology to generate adaptive strategies for games. Based on a set of low-level heuristics (or strategies), a hyper-heuristic game player can generate strategies which adapt to both the behaviour of the co-players and the game dynamics. By using a simple heuristic selection mechanism, a number of existing heuristics for specialised games can be integrated into an automated game player. As examples, we develop hyper-heuristic game players for three games: iterated prisoner's dilemma, repeated Goofspiel and the competitive traveling salesmen problem. The results demonstrate that a hyperheuristic game player outperforms the low-level heuristics, when used individually in game playing and it can generate adaptive strategies even if the low-level heuristics are deterministic. This methodology provides an efficient way to develop new strategies for games based on existing strategies.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.1109/TCIAIG.2015.2394780 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{lk2017, author = {J. Li and G. Kendall},
title = {A hyper-heuristic methodology to generate adaptive strategies for games},
journal = {IEEE Transactions on Computational Intelligence and AI in Games},
year = {2017},
volume = {9},
pages = {1--10},
number = {1},
abstract = {Hyper-heuristics have been successfully applied in solving a variety of computational search problems. In this study, we investigate a hyper-heuristic methodology to generate adaptive strategies for games. Based on a set of low-level heuristics (or strategies), a hyper-heuristic game player can generate strategies which adapt to both the behaviour of the co-players and the game dynamics. By using a simple heuristic selection mechanism, a number of existing heuristics for specialised games can be integrated into an automated game player. As examples, we develop hyper-heuristic game players for three games: iterated prisoner's dilemma, repeated Goofspiel and the competitive traveling salesmen problem. The results demonstrate that a hyperheuristic game player outperforms the low-level heuristics, when used individually in game playing and it can generate adaptive strategies even if the low-level heuristics are deterministic. This methodology provides an efficient way to develop new strategies for games based on existing strategies.},
doi = {10.1109/TCIAIG.2015.2394780},
issn = {1943-068X},
owner = {Graham},
timestamp = {2013.07.28},
webpdf = {http://www.graham-kendall.com/papers/lk2017.pdf} }