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Early Prediction of the Winner in StarCraft Matches

Topics: Adaptive Architectures and Mechanisms; Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World applications, Financial Applications, Neural Prostheses and Medical Applications, Neural based Data Mining and Complex Information Processing, Neural Network Software and Applications, Applications of Deep Neural networks, Robotics and Control Applications

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

ISBN: 978-989-758-274-5

ISSN: 2184-2825

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 wh ole match to predict its winner with high accuracy: with 10 minutes we can predict the outcome with 90% of accuracy. (More)

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Paper citation in several formats:
Álvarez-Caballero, A.; Merelo, J.; García Sánchez, P. and Fernández-Ares, A. (2017). Early Prediction of the Winner in StarCraft Matches.In Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI, ISBN 978-989-758-274-5, ISSN 2184-2825, pages 401-406. DOI: 10.5220/0006587304010406

author={Antonio Álvarez{-}Caballero. and J. J. Merelo. and Pablo García Sánchez. and A. Fernández{-}Ares.},
title={Early Prediction of the Winner in StarCraft Matches},
booktitle={Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,},


JO - Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,
TI - Early Prediction of the Winner in StarCraft Matches
SN - 978-989-758-274-5
AU - Álvarez-Caballero, A.
AU - Merelo, J.
AU - García Sánchez, P.
AU - Fernández-Ares, A.
PY - 2017
SP - 401
EP - 406
DO - 10.5220/0006587304010406

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