A master thesis from Aalborg University
Using Poisson Markov Models to Predict Game States in StarCraft
Author(s)
Term
4. term
Education
Publication year
2014
Submitted on
2014-03-06
Pages
76 pages
Abstract
This project aims to investigate the feasibility of using mixed observation types in Hidden Markov Models, in an attampt to increase the accuracy of recognising strategies, and predicting future actions in the domain of the real-time strategy game StarCraft. The types of observations in the model will be multinomial and Poisson distributions, and theory for both types of variables will be presented, as will theory for the combination of the two. The data for training the model in the StarCraft domain will be analysed to determine the validity of applyig Poisson distributions to the production of combat units. Finally, experiments will be made to determine the accuracy of predictions made by the model, as well as an evaluation of the most likely path through the state space of the model.
Keywords
Documents
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