| Summary: | 碩士 === 國立交通大學 === 資訊科學與工程研究所 === 100 === In this thesis, we propose a novel Team Ability Balancing System (TABS) to assist game designers to evaluate whether the team ability settings of any two teams are balanced in a role-playing combating game. TABS uses artificial neural network controllers which are trained by either genetic algorithm or particle swarm optimization. The best-trained controllers are chosen and applied for the team balance evaluation. Additionally, we also propose the Train-by-Coaches scheme which is useful for training controllers in a fair manner. In order to speed up the training process, we apply the multi-threading techniques to train the controllers in several independent game spaces in parallel. In a case study, we apply TABS to the in-house designed game, MagePowerCraft, for validations and experiments. Experimental results show that the performance of our system is quite satisfactory.
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