Abstract
In this paper, we examine the use of Artificial Neural Networks (ANNs) for designing an adaptive computer game system. This adaptive computer game system will enhance the game play experience of a player by adopting the concept of player centred game design. In this paper, the ANN is used to handle the dynamic difficulty level adjustment for each individual player. The difficulty level for each player can be customised using the proposed method, thus allowing game player to have a more personalised game play experience.
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Charles, D., McNeill, M., McAlister, M., Black, M., Moore, A., Stringer, K., Kücklich, J., Kerr, A.: Player-Centred Game Design: Player Modelling and Adaptive Digital Games. In: Proceedings of DiGRA 2005 Conference: Changing Views - Worlds in Play (2005)
Hunicke, R., Chapman, V.: AI for Dynamic Difficulty Adjustment in Games. In: Proceedings of Challenges in GameAI workshop, 19th National Conference on Artificial Intelligence (2004)
Wong, K.W., Fung, C.C., Depickere, A., Rai, S.: Static and Dynamic Difficulty Level Design for Edutainment Game Using Artificial Neural Networks. In: Proceedings of Edutainment 2006: International Conference on E-learning and Games, Hangzhou, China, April 2006, pp. 463–472 (2006)
Kennerly, D.: Better Game Design through Data Mining. Gamasutra.com (2003), http://www.gamasutra.com/features/20030815/kennerly_01.shtml
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning Interval Representation by Error Propagation. Parallel Distributed Processing 1, 318–362 (1986)
Wright, I., Marshall, J.: More AI in Less Processor Time: ‘Egocentric’ AI. Gamasutra.com (2000), http://www.gamasutra.com/features/20000619/wright_pfv.htm
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© 2008 Springer-Verlag Berlin Heidelberg
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Wong, K.W. (2008). Adaptive Computer Game System Using Artificial Neural Networks. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4985. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69162-4_70
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DOI: https://doi.org/10.1007/978-3-540-69162-4_70
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69159-4
Online ISBN: 978-3-540-69162-4
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