Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Neural Network that Learns to Play Five-in-a-Row
ANNES '95 Proceedings of the 2nd New Zealand Two-Stream International Conference on Artificial Neural Networks and Expert Systems
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A Theory of Fun for Game Design
A Theory of Fun for Game Design
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In recent years, many researchers are attracted to computer games research. Capable gamers can easily get bored, while beginners tend to give up after trying several times because the game does not correspond to their level of interest. Therefore, this paper proposes that the user's play pattern to be modeled on the basis of probability and level designer will dynamically generates the gaming level accordingly. We analyze user's play pattern and design pattern based on GMM (probability model) and dynamically generate the level with online learning technique adapting the reinforcement technique. The play pattern is modeled using GMM and in order to create game level dynamically, the method of updating the weight of enemy creation using online script is proposed. Finally, we apply our proposed method to a 2D shooting game and introduce user's play pattern leading to design pattern in the game.