Predicting Player Strategies in Real Time Strategy Games
Translated title
Authors
Term
1. term
Education
Publication year
2010
Submitted on
2010-12-20
Pages
54
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.
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