Thesis Topic Details

Topic ID:
3552
Title:
Local Search Techniques for General Single-Player Games
Supervisor:
Michael Thielscher
Research Area:
Artificial Intelligence
Associated Staff
Assessor:
Serge Gaspers
Topic Details
Status:
Active
Type:
R & D
Programs:
CS CE BIOM BINF SE
Group Suitable:
Industrial:
No
Pre-requisites:
COMP3411 Artificial Intelligence
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.

This project aims at developing a GGP for single-player games that uses a local search technique such as Hill Climbing to solve general games. It involves developing and implementing a method to automatically translate game descriptions into a suitable optimisation problem.

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
Past Student Reports
 
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