Thesis Topic Details

Topic ID:
3550
Title:
Applying Informed Search Methods to General Single-Player Games
Supervisor:
Michael Thielscher
Research Area:
Artificial Intelligence
Associated Staff
Assessor:
Alan Blair
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.

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. This project aims at developing a GGP for single-player games that uses an informed search method such as A* to solve general games. It involves developing and implementing a method to automatically generate admissible heuristics from the rules of a game.

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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