Abstract
Development of real time strategy game AI is a challenging and difficult task because of the real-time constraint and the large search space in finding the best strategy. In this paper, we propose a machine learning approach based on genetic algorithm and artificial neural network to develop a neural-evolutionary model for case-based planning in real time strategy (RTS) games. This model provides efficient, fair and natural game AI to tackle the RTS game problems. Experimental results are provided to support our idea. This model could be integrated with warbots in battlefields, either real or synthetic ones, in the future for mimic human like behaviors.
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© 2009 Springer-Verlag Berlin Heidelberg
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Niu, B., Wang, H., Ng, P.H.F., Shiu, S.C.K. (2009). A Neural-Evolutionary Model for Case-Based Planning in Real Time Strategy Games. In: Chien, BC., Hong, TP., Chen, SM., Ali, M. (eds) Next-Generation Applied Intelligence. IEA/AIE 2009. Lecture Notes in Computer Science(), vol 5579. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02568-6_30
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DOI: https://doi.org/10.1007/978-3-642-02568-6_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02567-9
Online ISBN: 978-3-642-02568-6
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