Authors:
Antonio Álvarez-Caballero
1
;
J. J. Merelo
1
;
Pablo García Sánchez
2
and
A. Fernández-Ares
1
Affiliations:
1
University of Granada, Spain
;
2
University of Cádiz, Spain
Keyword(s):
Prediction, Classification, Strategy, Planification.
Related
Ontology
Subjects/Areas/Topics:
Adaptive Architectures and Mechanisms
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
A fast and precise prediction of the outcome of a game is essential for the design of bots that play the game;
it can be used either offline as a fast way to design bot strategies or online for conserving resources and
conceding defeat or speed up victory, as well as evaluating the consequences of actions. The objective of this
paper is predicting the winner of a StarCraft match as soon as possible. This study is done with supervised
learning, because a lot of suitable data is available. The main problem of this approach is the big amount of
generated data, so it has to be selected and organised properly and be treated with proper tools. A set of six
learning algorithms is used, from simpler ones to more complex algorithms. Spark and MLlib are used due to
their capabilities to deal with big amounts of data. With the learned models, time of matches are restricted,
trying to get a time bound for predicting results. With this approach we get that it is not necessary to play a
w
hole match to predict its winner with high accuracy: with 10 minutes we can predict the outcome with 90%
of accuracy.
(More)