Thesis Topic Details

Topic ID:
3554
Title:
Belief Revision for General Game Playing
Supervisor:
Michael Thielscher
Research Area:
Artificial Intelligence
Associated Staff
Assessor:
Maurice Pagnucco
Topic Details
Status:
Active
Type:
Research
Programs:
CS CE BIOM BINF SE
Group Suitable:
No
Industrial:
No
Pre-requisites:
COMP3411 Artificial Intelligence, COMP4418 Knowledge Representation or equivalent
Description:
General game players (GGPs) are computer systems able to play strategy games based solely on formal game descriptions supplied at "runtime" (in other words, they don't know the rules until the game starts). Unlike specialised game players, such as Deep Blue, general game players cannot rely on algorithms designed in advance for specific games; they must discover such algorithms themselves.

General game playing expertise depends on intelligence on the part of the game player and not just intelligence of the programmer of the game player. A crucial aspect for making better decisions is to model your opponent. This project aims at researching the application of belief revision methods to represent and continually adapt the models of opponent.

If successful, this research could be published in a high-profile conference or journal.
Comments:
M. Genesereth, M. Thielscher. General Game Playing. Synthesis Lectures on Artificial Intelligence and Machine Learning 2014.
http://www.morganclaypool.com/doi/abs/10.2200/S00564ED1V01Y201311AIM024

M. Genesereth, N. Love, B. Pell: General Game Playing: Overview of the AAAI Competition, AAAI Magazine, Spring 2005.
http://logic.stanford.edu/classes/cs227/2014/readings/aaai.pdf
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