A master thesis from Aalborg University
Strategy Prediction in StarCraft: Brood War using Multilayer Perceptrons
Author(s)
Term
2. term
Education
Publication year
2011
Submitted on
2011-10-28
Pages
98 pages
Abstract
In this project we first formulate a theory of strategy prediction in real-time strategy games based the notion that a players strategical op- tions are dependent on the in-game assets avail- able. We then test a number of common training algorithms to train multi-layer perceptrons to predict the in-game assets of a player at time n given information about information about the same players in-game assets at time m, m < n. A novel feature selection technique based on real-time strategy game design principles is in- troduced and tested, to address the number of possible input features that can be extracted from in-game assets. A series of experiments to evaluate the strategy prediction performance are performed on MLPs trained with the Backpropagation, RProp, Genetic Algorithm and two Genetic Algorithm hybrids using Backpropagation and RProp.
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.