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

A Conversation article celebrating Pi
http://bit.ly/1DXuXbV
How are football fixtures worked out?
http://bit.ly/1z0oTAH

Latest Blog Post

Snooker: Celebrating 40 years at the Crucible

Random Blog Post

Football (Soccer) Prediction: Development Framework (#001)

Publication(s)

A triple objective function with a Chebychev dynamic pick-and-place point specification approach to optimise the surface mount placement machine
http://bit.ly/fi7zmk
Channel Assignment Optimisation Using a Hyper-Heuristic
http://bit.ly/eIazTf
Population based Monte Carlo tree search hyper-heuristic for combinatorial optimization problems
http://bit.ly/1IIArdQ
Frequency analysis for dendritic cell population tuning
http://bit.ly/go1Ihk

Graham Kendall: Details of Requested Publication


Citation

Babadi, A; Omoomi, B and Kendall, G EnHiC: An enforced hill climbing based system for general game playing. In Proceedings of the 2015 IEEE Conference on Computational Intelligence and Games (CIG), pages 193-199, 2015.


Abstract

Accurate decision making in games has always been a very complex and yet interesting problem in Artificial Intelligence (AI). General video game playing (GVGP) is a new branch of AI whose target is to design agents that are able to win in every unknown game environment by choosing wise decisions. This paper proposes a new search methodology based on enforced hill climbing for using in GVGP and we evaluate its performance on the benchmarks of the general video game AI competition (GVG-AI). Also a simple and efficient heuristic function for GVGP is proposed. The results show that EnHiC outperforms several well-known and successful methods in the GVG-AI competition.


pdf

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doi

The doi for this publication is 10.1109/CIG.2015.7317907 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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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{bok2015, author = {A. Babadi and B. Omoomi and G. Kendall},
title = {EnHiC: An enforced hill climbing based system for general game playing},
booktitle = {Proceedings of the 2015 IEEE Conference on Computational Intelligence and Games (CIG)},
year = {2015},
pages = {193--199},
abstract = {Accurate decision making in games has always been a very complex and yet interesting problem in Artificial Intelligence (AI). General video game playing (GVGP) is a new branch of AI whose target is to design agents that are able to win in every unknown game environment by choosing wise decisions. This paper proposes a new search methodology based on enforced hill climbing for using in GVGP and we evaluate its performance on the benchmarks of the general video game AI competition (GVG-AI). Also a simple and efficient heuristic function for GVGP is proposed. The results show that EnHiC outperforms several well-known and successful methods in the GVG-AI competition.},
doi = {10.1109/CIG.2015.7317907},
owner = {kzzgrk},
timestamp = {2016.01.07},
webpdf = {http://www.graham-kendall.com/papers/bok2015.pdf} }