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

How are football fixtures worked out?
http://bit.ly/1z0oTAH
I am a member of the Automated Scheduling, Optimisation and Planning Research Group
http://bit.ly/eIQ5XC

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

Random Blog Post

Football fixture forecasting. Are you any good?

Publication(s)

A multi-agent based simulated stock market - testing on different types of stocks
http://bit.ly/haEt18
An artificial neural network for predicting domestic hot water characteristics
http://bit.ly/dNtSFu
An efficient guided local search approach for service network design problem with asset balancing
http://bit.ly/fY7uch
Comparison of meta-heuristic algorithms for clustering rectangles
http://bit.ly/eQQ0Kd

Graham Kendall: Details of Requested Publication


Citation

Li, J and Kendall, G A Strategy with Novel Evolutionary Features for the Iterated Prisoner's Dilemma. Evolutionary Computation, 17 (2): 257-274, 2009.


Abstract

In recent iterated prisoner's dilemma tournaments, the most successful strategies were those that had identification mechanisms. By playing a predetermined sequence of moves and learning from their opponents' responses, these strategies managed to identify their opponents. We believe that these identification mechanisms may be very useful in evolutionary games. In this paper one such strategy, which we call collective strategy, is analyzed. Collective strategies apply a simple but efficient identification mechanism (that just distinguishes themselves from other strategies), and this mechanism allows them to only cooperate with their group members and defect against any others. In this way, collective strategies are able to maintain a stable population in evolutionary iterated prisoner's dilemma. By means of an invasion barrier, this strategy is compared with other strategies in evolutionary dynamics in order to demonstrate its evolutionary features. We also find that this collective behavior assists the evolution of cooperation in specific evolutionary environments.


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doi

The doi for this publication is 10.1162/evco.2009.17.2.257 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 (2.366), 2013 (3.733), 2012 (2.109), 2011 (1.061), 2010 (2.630), 2009 (3.103), 2008 (3.000), 2007 (1.575), 2006 (1.325), 2005 (1.568), 2004 (3.206), 2003 (2.395)

URL

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Bibtex

@ARTICLE{lk2009, author = {J. Li and G. Kendall},
title = {A Strategy with Novel Evolutionary Features for the Iterated Prisoner's Dilemma},
journal = {Evolutionary Computation},
year = {2009},
volume = {17},
pages = {257--274},
number = {2},
abstract = {In recent iterated prisoner's dilemma tournaments, the most successful strategies were those that had identification mechanisms. By playing a predetermined sequence of moves and learning from their opponents' responses, these strategies managed to identify their opponents. We believe that these identification mechanisms may be very useful in evolutionary games. In this paper one such strategy, which we call collective strategy, is analyzed. Collective strategies apply a simple but efficient identification mechanism (that just distinguishes themselves from other strategies), and this mechanism allows them to only cooperate with their group members and defect against any others. In this way, collective strategies are able to maintain a stable population in evolutionary iterated prisoner's dilemma. By means of an invasion barrier, this strategy is compared with other strategies in evolutionary dynamics in order to demonstrate its evolutionary features. We also find that this collective behavior assists the evolution of cooperation in specific evolutionary environments.},
doi = {10.1162/evco.2009.17.2.257},
issn = {1063-6560},
keywords = {Iterated Prisoners Dilemma, Collective Strategy, Identification mechanism},
owner = {est},
timestamp = {2010.02.22} }