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CURRENT MEMBERS

Below is a list of the masters members actively conducting research at CIRG. Click on each name for more detailed information on the researcher and his/her project.


A van Wyk

M.Sc Started in 2009
Hons-B.Sc Completed in 2008

S van Eeden

M.Sc Started in 2009

B Anguelov

M.Sc Started in 2009
Hons-B.Sc Completed in 2008

PG Ferreira

M.Sc Started in 2009

T Scheepers

M.Sc Started in 2008

M Riekert

M.Sc Started in 2008
Hons-B.Sc Completed in 2007

J Nicholls

M.Sc Started in 2008

T Naidoo

M.Sc Started in 2008

W Matthysen

M.Sc Started in 2008
Hons-B.Sc Completed in 2007

L Langenhoven

M.Sc Started in 2008
Hons-B.Sc Completed in 2006

M Da Silva

M.Sc Started in 2008

R Vlietstra

M.Sc Started in 2007

M van der Merwe

M.Sc Started in 2007

M Smit

M.Sc Started in 2007

A Rakitianskaia

M.Sc Started in 2007

J Duhain

M.Sc Started in 2007

A Louis

M.Sc Started in 2006

R Klazar

M.Sc Started in 2006

T Cloete

M.Sc Started in 2006

A Hauptfleisch

M.Sc Started in 2006

R Brink

M.Sc Started in 2006

S Allen

M.Sc Started in 2005
Hons-B.Sc Completed in 2004

G Pampara

M.Sc Started in 2005
Hons-B.Sc Completed in 2004

A Edwards

M.Sc Started in 2005

A Brenner

M.Sc Started in 2005

E Papacostantis

M.Sc Started in 2004
Hons-B.Sc Completed in 2003

D Barla-Szabo

M.Sc Started in 2003
Hons-B.Sc Completed in 2002

E Dean

M.Sc Started in 2002

W van Heerden

M.Sc Started in 2002

E Basson

M.Sc Started in 1999
Hons-B.Sc Completed in 1998

ALUMNI

Below is a list of previous masters members that conducted research at CIRG. Click on each name for more detailed information on the researcher and his/her project.


M Poggiolini

M.Sc Completed in 2009

F Zablocki

M.Sc Completed in 2008

J Pun

M.Sc Completed

C Naicker

PhD Started in 2007
M.Sc Completed in 2006
Hons-B.Sc Completed in 2002

H Grobler

M.Sc Completed in 2005
Hons-B.Sc Completed in 2004

L Messerschmidt

M.Sc Completed

M Neethling

M.Sc Completed in 2008

W Duminy

M.Sc Completed in 2007

J du Plessis

M.Sc Completed in 2005

E Peer

M.Sc Completed in 2005

G Nel

M.Sc Completed in 2005

N Franken

PhD Started in 2005
M.Sc Completed in 2004
Hons-B.Sc Completed in 2002

A Graaff

PhD Started in 2005
M.Sc Completed in 2003

R Brits

M.Sc Completed in 2003
Hons-B.Sc Completed in 2000

U Paquet

M.Sc Completed in 2003

G Potgieter

M.Sc Completed in 2003
Hons-B.Sc Completed in 2001

D van Wyk

M.IT Completed in 2003

A Ismail

PhD Started in 2005
M.Sc Completed in 2001

D Rodic

PhD Completed in 2005
M.Sc Completed in 1999

A Adejumo

M.Sc Completed in 1999

MEMBER PROFILE



 Name:

 Willem Duminy

Portrait photo

 E-mail:

 wduminy@mweb.co.za

 Group(s):

 Evolutionary Computation
 Games

 

 Degree specific information: M.Sc

 Title:

 An evolutionary approach to learn the components of evaluation functions

 Abstract:

The three topics: evolutionary computing, machine learning and perfect information games combine in an experiment that investigates a method to increase the skill of a game-playing agent from zero game knowledge. The research entails the definition of a learning framework that makes use of genetic- and evolutionary- techniques to improve the performance two-person perfect-information game agents. Initially, the agent is equipped with the game rules and the ability to "percieve" elementary properties of a game state. A example of such a property might be "how many of my kings are on row four of the board". The properties are combined using a set of primitive operations to form features that are used in the evaluation function. The work of the learning agent is to discover these features and then determine the weights assigned to the features for optimal playing performance. The idea is to start with no game knowledge and through repeated application of the discovery step and weight optimisation step, the agent will gradually improve. The features and the weights can be seen as two genes that adapt and influence each other during the learning process. Individuals with strong features cooperate in a co-evolutionary fashion with those with strong weights. Competitive co-evolution ensures the improvement of a single gene: playing agents against each other to improve weights; or eliminating weaker feature sets.

 Supervisor / Co-Supervisor:

 AP Engelbrecht

 Thesis:

 Not available for download yet.




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