Abstract
This paper addresses how intelligent characters, having learning capability based on the neural network technology, automatically adapt to environmental changes in computer games. Our adaptation solution includes an autonomous adaptation scheme and a cooperative adaptation scheme. With the autonomous adaptation scheme, each intelligent character steadily assesses changes of its game environment while taking into consideration recently earned scores, and initiates a new learning process when a change is detected. Intelligent characters may confront various opponents in many computer games. When each intelligent character has fought with just part of the opponents, the cooperative adaptation scheme, based on a genetic algorithm, creates new intelligent characters by composing their partial knowledge of the existing intelligent characters. The experimental results show that intelligent characters can properly accommodate to the changes with the proposed schemes.
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Laird, J.E., van Lent, M.: Human-level AI’s killer application: Interactive computer games. AI Magazine 22, 15–26 (2001)
Forbus, K.D., Laird, J.E.: Guest editors’ introduction: AI and the entertainment industry. IEEE Intelligent Systems 17, 15–16 (2002)
Fairclough, C., Fagan, M., Namee, B.M., Lippmann, R.P.: Research directions for AI in computer games. Technical report, Computer Science Department, Trinity College Dublin (2001)
Forbus, K.D., Mahoney, J.V., Dill, K.: How qualitative spatial reasoning can improve strategy game AIs. IEEE Intelligent Systems 17, 25–30 (2002)
Bauckhage, C., Thurau, C., Sagerer, G.: Learning human-like opponent behavior for interactive computer games. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 148–155. Springer, Heidelberg (2003)
Fogel, D.: Using evolutionary programming to create neural networks that are capable of playing tic-tac-toe. In: IEEE International Conference on Neural Networks, pp. 875–880 (1993)
Freisleben, B.: A neural network that learns to play five-in-a-row. In: 2nd New Zealand Two-Stream International Conference on Artificial Neural Networks and Expert Systems, pp. 87–90 (1995)
Moriarty, D.E., Miikkulainen, R.: Discovering complex othello strategies through evolutionary neural networks. Connection Science 7, 195–210 (1995)
Fogel, D.B., Hays, T.J., Hahn, S.L., Quon, J.: A self-learning evolutionary chess program. Proceedings of IEEE 92, 1947–1954 (2004)
Richards, N., Moriarty, D.E., Miikkulainen, R.: Evolving neural networks to play go. Applied Intelligence 8, 85–96 (1998)
Cho, B.H., Jung, S.H., Seong, Y.R., Oh, H.R.: Exploiting intelligence in fighting action games using neural networks. Submitted to IEICE Transactions on Information and Systems
Lippmann, R.P.: An introduction to computing with neural nets. IEEE ASSP Magazine 4, 4–22 (1987)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Professional, Reading (1989)
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© 2005 Springer-Verlag Berlin Heidelberg
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Cho, B.H., Jung, S.H., Shim, KH., Seong, Y.R., Oh, H.R. (2005). Adaptation of Intelligent Characters to Changes of Game Environments. In: Hao, Y., et al. Computational Intelligence and Security. CIS 2005. Lecture Notes in Computer Science(), vol 3801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596448_159
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DOI: https://doi.org/10.1007/11596448_159
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30818-8
Online ISBN: 978-3-540-31599-5
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