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 few papers on Sports Scheduling.
http://bit.ly/gVaUqT
I have published a few papers on Sports Scheduling.
http://bit.ly/gVaUqT

Latest Blog Post

How Isaac Newton could help you beat the casino at roulette

Random Blog Post

The Monty Hall Problem

Publication(s)

A Tabu Search hyper-heuristic strategy for t-way test suite generation
http://bit.ly/1W2GjGR
On Nie-Tan operator and type-reduction of interval type-2 fuzzy sets
http://bit.ly/2kqxtD3
Competitive travelling salesmen problem: A hyper-heuristic approach
http://bit.ly/KgQhlh
Automated tile design for self-assembly conformations
http://bit.ly/h7QYiX

Graham Kendall: Details of Requested Publication


Citation

Munoz-Avila, H; Bauckhage, C; Bida, M; Congdon, C. B. and Kendall, G Learning and Game AI. In Artificial and Computational Intelligence in Games: A Follow-up to Dagstuhl Seminar 12191, pages 33-43, 2012.

This was a follow up publication to a Dagstuhl seminar


Abstract

The incorporation of learning into commercial games can enrich the player experience, but may concern developers in terms of issues such as losing control of their game world. We explore a number of applied research and some fielded applications that point to the tremendous possibilities of machine learning research including game genres such as real-time strategy games, flight simulation games, car and motorcycle racing games, board games such as Go, an even traditional game-theoretic problems such as the prisoners dilemma. A common trait of these works is the potential of machine learning to reduce the burden of game developers. However a number of challenges exists that hinder the use of machine learning more broadly. We discuss some of these challenges while at the same time exploring opportunities for a wide use of machine learning in games.


pdf

You can download the pdf of this publication from here


doi

The doi for this publication is 10.4230/DFU.Vol6.12191.33 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

The URL for additional information is http://drops.dagstuhl.de/portals/dfu/index.php?semnr=13017

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

@INBOOK{mbbbk2012, chapter = {Artificial and Computational Intelligence in Games: A Follow-up to Dagstuhl Seminar 12191},
pages = {33--43},
title = {Learning and Game AI},
year = {2012},
editor = {S.M. Lucas and M. Mateas and M. Preuss and P. Spronck and J. Togelius},
author = {H. Munoz-Avila and C. Bauckhage and M. Bida and C. Bates Congdon and G. Kendall},
volume = {6},
note = {This was a follow up publication to a Dagstuhl seminar},
abstract = {The incorporation of learning into commercial games can enrich the player experience, but may concern developers in terms of issues such as losing control of their game world. We explore a number of applied research and some fielded applications that point to the tremendous possibilities of machine learning research including game genres such as real-time strategy games, flight simulation games, car and motorcycle racing games, board games such as Go, an even traditional game-theoretic problems such as the prisoners dilemma. A common trait of these works is the potential of machine learning to reduce the burden of game developers. However a number of challenges exists that hinder the use of machine learning more broadly. We discuss some of these challenges while at the same time exploring opportunities for a wide use of machine learning in games.},
doi = {10.4230/DFU.Vol6.12191.33},
owner = {Graham},
timestamp = {2013.12.27},
url = {http://drops.dagstuhl.de/portals/dfu/index.php?semnr=13017},
webpdf = {http://www.graham-kendall.com/papers/mbbbk2012.pdf} }