Strategy Prediction in StarCraft: Brood War using Multilayer Perceptrons
Translated title
Authors
Term
2. term
Education
Publication year
2011
Submitted on
2011-10-28
Pages
98
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.
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