Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/64335
Title: A neural-evolutionary model for case-based planning in real time strategy games
Authors: Niu, B
Wang, HB
Ng, HFP
Shiu, CKS 
Issue Date: 2009
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), v. 5579, p. 291-300
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.
Keywords: Case-based planning
Real time strategy (RTS) games
Genetic algorithm
Artificial neural network
Publisher: Springer
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
ISBN: 978-3-642-02567-9 (print)
978-3-642-02568-6 (online)
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-642-02568-6_30
Description: 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2009, Tainan, Taiwan, June 24-27, 2009
Appears in Collections:Conference Paper

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