A Neural-Evolutionary Model for Case-Based Planning in Real Time Strategy Games

Authors:
Ben Niu;Haibo Wang;Peter H. Ng;Simon C. Shiu
Affiliations:
Department of Computing, The Hong Kong Polytechnic University, Hong Kong, P.R. China;Department of Computing, The Hong Kong Polytechnic University, Hong Kong, P.R. China;Department of Computing, The Hong Kong Polytechnic University, Hong Kong, P.R. China;Department of Computing, The Hong Kong Polytechnic University, Hong Kong, P.R. China
Venue:
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Year:
2009

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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.