Abstract
To meet the requirement of real time strategy (RTS) games, this paper proposes a two-stage speed up model using the combination of genetic algorithm and ant algorithm (GA-AA) and then artificial neural network (ANN) in a RTS game. The task is to tackle the optimization and adaptive defensive positioning game problems. In the first stage, we use GA to perform the initial optimization, and then AA is incorporated to speed up GA. In the second stage, the results of GA-AA can be used as cases to train an ANN, which obtains the optimal solutions very fast and then completes the whole off-line learning process. These optimal solutions stored in the trained ANN are considered to be still useful to provide good solutions in even random generated environments. Thus the two-stage speed up model not only needs less off-line training time, but also can recommend good on-line solutions very quickly. Experimental results are demonstrated to support our idea.
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Li, Y., Tong, X., Dai, J. (2011). A Two-Stage Speed Up Model for Case-Based Learning in Real Time Strategy Games. In: Zhang, J. (eds) Applied Informatics and Communication. ICAIC 2011. Communications in Computer and Information Science, vol 226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23235-0_1
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DOI: https://doi.org/10.1007/978-3-642-23235-0_1
Publisher Name: Springer, Berlin, Heidelberg
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