Title: Game team balancing by using particle swarm optimization
Authors: Fang, Shih-Wei
Wong, Sai-Keung
Department of Computer Science
Keywords: Artificial neural network;Particle swarm optimization;Game balance;Role-playing game;Team balancing system
Issue Date: 1-Oct-2012
Abstract: Game balancing affects the gaming experience of players in video-games. In this paper, we propose a novel system, team ability balancing system (TABS), which is developed for automatically evaluating the performance of two teams in a role-playing video game. TABS can be used for assisting game designers to improve team balance. In TABS, artificial neural network (ANN) controllers learn to play the game in an unsupervised manner and they are evolved by using particle swarm optimization. The ANN controllers control characters of the two teams to fight with each other. An evaluation method is proposed to evaluate the performance of the two teams. Based on the evaluation results, the game designers can adjust the abilities of the characters so as to achieve team balance. We demonstrate TABS for our in-house MagePowerCraft game in which each team consists of up to three characters. (C) 2012 Elsevier B.V. All rights reserved.
URI: http://dx.doi.org/10.1016/j.knosys.2012.02.017
ISSN: 0950-7051
DOI: 10.1016/j.knosys.2012.02.017
Volume: 34
Begin Page: 91
End Page: 96
Appears in Collections:Articles

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