Browsing by Author "Fang, Shih-Wei"

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A Study on Genetic Algorithm and Neural Network for Implementing Mini-Games
(2010-01-01)
Charoenkwan, Phasit
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Fang, Shih-Wei
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Wong, Sai-Keung
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資訊工程學系
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Department of Computer Science
We study on artificial neural network-based controllers which are either trained or evolved by using the supervised or unsupervised learning approach. We employed backpropagation for the supervised method and the genetic algorithm for the unsupervised method. After training the controllers, we applied the controllers to our three newly designed mini-3D games. We performed a comprehensive study on the performance and weaknesses of the controllers. We emerged the controllers as fundamental tools for giving us more understanding about artificial neural network and its effectiveness in imitating players' behaviours.
A Study on Genetic Algorithm and Neural Network for Mini-Games
(2012-01-01)
Wong, Sai-Keung
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Fang, Shih-Wei
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資訊工程學系
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Department of Computer Science
Physics simulation and character control are two important issues in computer games. In this paper, we propose two games which are tailored for investigating some aspects of these two issues. We study on the applications of neural network and the genetic algorithm techniques for building the controllers and the controllers should be able to finish the specific tasks in the two games. The goal of the first game is that the controller can shoot a ball so that the ball collides with the other two balls one after another. The challenge of this game is that the ball should be shot from the proper position and the goal is achieved every time. The second game is a duel game and two virtual characters are controlled to fight with each other. We develop a method for verifying whether or not the skill power of the two virtual characters is balanced. The controllers of both games are evolved based on neural network and genetic algorithm in an unsupervised learning manner. We perform a comprehensive study on the performance and weaknesses of the controllers.
Game team balancing by using particle swarm optimization
(2012-10-01)
Fang, Shih-Wei
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Wong, Sai-Keung
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資訊工程學系
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Department of Computer Science
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.