A master thesis from Aalborg University
Predicting Player Strategies in Real Time Strategy Games
Author(s)
Term
1. term
Education
Publication year
2010
Submitted on
2010-12-20
Pages
54 pages
Abstract
This paper examines opponent modeling in the real-time strategy game StarCraft. Actual game replays are used to identify similar player strategies via unsupervised QT clustering as an alternative to relying on expert knowledge for identifying strategies. We then predict the strategy of a human player using two well-known classifiers, artificial neural networks and Bayesian networks, in addition to our own novel approach called Action- Trees. Finally we look at the classifiers’ ability to accurately predict player strategies given both complete and incomplete training data, and also when the training set is reduced in size.
Keywords
Documents
Colophon: This page is part of the AAU Student Projects portal, which is run by Aalborg University. Here, you can find and download publicly available bachelor's theses and master's projects from across the university dating from 2008 onwards. Student projects from before 2008 are available in printed form at Aalborg University Library.
If you have any questions about AAU Student Projects or the research registration, dissemination and analysis at Aalborg University, please feel free to contact the VBN team. You can also find more information in the AAU Student Projects FAQs.