Strategy Prediction in StarCraft: Brood War using Multilayer Perceptrons

Student thesis: Master thesis (including HD thesis)

  • Henrik Otte Sørensen
  • Johannes Garm Nielsen
2. term, Computer Science, Master (Master Programme)
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
Publication date28 Oct 2011
Number of pages98
ID: 56560823