Abstract
“Fun” is the most important determinant of whether a game will be successful. Fun can come from challenges and goals, such as victory in a scenario, the accumulation of money, or the right to move to the next level. A game that provides a satisfying level of challenge is said to be balanced. Some researchers use artificial intelligence (AI) on the dynamic game balancing. They use reinforcement learning and focuses on the non-player characters. However, this is not suitable for all game genres such as a game requiring dynamic terrains. We propose to adjust the difficulty of a game level by mining and applying data about the sequential patterns of past player behavior. We compare the performance of the proposed approach on a maze game against approaches using other types of game AI. Positive feedback and these comparisons show that the proposed approach makes the game both more interesting and more balanced.
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© 2008 Springer-Verlag Berlin Heidelberg
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Chiu, K.S.Y., Chan, K.C.C. (2008). Using Data Mining for Dynamic Level Design in Games. In: An, A., Matwin, S., Raś, Z.W., Ślęzak, D. (eds) Foundations of Intelligent Systems. ISMIS 2008. Lecture Notes in Computer Science(), vol 4994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68123-6_69
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DOI: https://doi.org/10.1007/978-3-540-68123-6_69
Publisher Name: Springer, Berlin, Heidelberg
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