کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
402412 | 676936 | 2012 | 6 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Game team balancing by using particle swarm optimization Game team balancing by using particle swarm optimization](/preview/png/402412.png)
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.
Journal: Knowledge-Based Systems - Volume 34, October 2012, Pages 91–96