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 am a member of the Automated Scheduling, Optimisation and Planning Research Group
http://bit.ly/eIQ5XC
I have published a number of papers on Cutting and Packing
http://bit.ly/dQPw7T

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Singapore Statement on Research Integrity

Publication(s)

Managing University-Industry Collaborations in Malaysia by Examining its Critical Success Factors: A Dyadic Approach
RATE_LIMIT_EXCEEDED
Hyperheuristics: A Tool for Rapid Prototyping in Scheduling and Optimisation
RATE_LIMIT_EXCEEDED
Using Harmony Search with Multiple Pitch Adjustment Operators for the Portfolio Selection Problem
RATE_LIMIT_EXCEEDED
The Cross-domain Heuristic Search Challenge - An International Research Competition
RATE_LIMIT_EXCEEDED

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

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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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} }