DocumentCode :
2221820
Title :
Optimizing player behavior in a real-time strategy game using evolutionary algorithms
Author :
Fernández-Ares, A. ; Mora, A.M. ; Merelo, J.J. ; García-Sánchez, P. ; Fernandes, C.
Author_Institution :
Dept. de Arquitectura y Tecnol. de Comput., Univ. of Granada, Granada, Spain
fYear :
2011
fDate :
5-8 June 2011
Firstpage :
2017
Lastpage :
2024
Abstract :
This paper describes an Evolutionary Algorithm for evolving the decision engine of a bot designed to play the Planet Wars game. This game, which has been chosen for the Google Artificial Intelligence Challenge in 2010, requires that the artificial player is able to deal with multiple objectives, while achieving a certain degree of adaptability in order to defeat different opponents in different scenarios. The decision engine of the bot is based on a set of rules that have been defined after an empirical study. Then, an Evolutionary Algorithm is used for tuning the set of constants, weights and probabilities that define the rules, and, therefore, the global behavior of the bot. The paper describes the Evolutionary Algorithm and the results attained by the decision engine when competing with other bots. The proposed bot defeated a baseline bot in most of the playing environments and obtained a ranking position in top-20% of the Google Artificial Intelligence competition.
Keywords :
computer games; evolutionary computation; optimisation; Planet Wars game; decision engine; evolutionary algorithms; player behavior optimisation; real-time strategy game; Artificial intelligence; Engines; Evolutionary computation; Games; Genetic algorithms; Google; Planets;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation (CEC), 2011 IEEE Congress on
Conference_Location :
New Orleans, LA
ISSN :
Pending
Print_ISBN :
978-1-4244-7834-7
Type :
conf
DOI :
10.1109/CEC.2011.5949863
Filename :
5949863
Link To Document :
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