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
A Conversation article celebrating Pi
http://bit.ly/1DXuXbV

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

Random Blog Post

Wildcards and Carriage Returns in Word

Publication(s)

Evolving Reusable 3D Packing Heuristics with Genetic Programming.
http://bit.ly/e75y7F
Chapter 7: Opponent Modelling, Evolution, and the Iterated Prisoner's Dilemma
http://bit.ly/1eJzj8n
Learning versus Evolution in Iterated Prisoner's Dilemma
http://bit.ly/eWgsgR
Regulators as Ďagentsí: power and personality in risk regulation and a role for agent-based simulation
http://bit.ly/evaXWn

Graham Kendall: Details of Requested Publication


Citation

Kendall, G and Su, Y Learning with imperfections - a multi-agent neural-genetic trading system with differing levels of social learning. In Proceedings of the 2004 IEEE Conference on Cybernetics and Intelligent Systems (CIS 2004), pages 47-52, Singapore, 2004.


Abstract

Some real life dynamic systems are so large and complex that the individuals inside the system can only partially understand their environment. In other words, the dynamic environment is imperfect to its participants. In this paper, by using the stock market as a test bed, we demonstrate an integrated individual learning and social learning model for optimisation problems in dynamic environments with imperfect information. By applying differing levels of social learning process in an evolutionary simulated stock market, we study the importance of social learning on the adaptability of artificial agents in imperfect environments. Comparisons between the integrated individual and social learning model and other evolutionary approaches for dynamic optimisation problems, particularly the memory-based approaches and multipopulation approaches, are also drawn with the emphasis on optimisation problems with imperfect information.


pdf

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doi

The doi for this publication is 10.1109/ICCIS.2004.1460385 You can link directly to the original paper, via the doi, from here

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Bibtex

@INPROCEEDINGS{ks2004, author = {G. Kendall and Y. Su},
title = {Learning with imperfections - a multi-agent neural-genetic trading system with differing levels of social learning},
booktitle = {Proceedings of the 2004 IEEE Conference on Cybernetics and Intelligent Systems (CIS 2004)},
year = {2004},
pages = {47--52},
address = {Singapore},
month = {1-3 December},
abstract = {Some real life dynamic systems are so large and complex that the individuals inside the system can only partially understand their environment. In other words, the dynamic environment is imperfect to its participants. In this paper, by using the stock market as a test bed, we demonstrate an integrated individual learning and social learning model for optimisation problems in dynamic environments with imperfect information. By applying differing levels of social learning process in an evolutionary simulated stock market, we study the importance of social learning on the adaptability of artificial agents in imperfect environments. Comparisons between the integrated individual and social learning model and other evolutionary approaches for dynamic optimisation problems, particularly the memory-based approaches and multipopulation approaches, are also drawn with the emphasis on optimisation problems with imperfect information.},
doi = {10.1109/ICCIS.2004.1460385},
keywords = {trading, genetic algorithms, stick market, agents, imperfect environments},
timestamp = {2007.03.29},
webpdf = {http://www.graham-kendall.com/papers/ks2004.pdf} }