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A fast evaluation method for RTS game strategy using fuzzy extreme learning machine

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Abstract

This paper proposes a fast learning method for fuzzy measure determination named fuzzy extreme learning machine (FELM). Moreover, we apply it to a special application domain, which is known as unit combination strategy evaluation in real time strategy (RTS) game. The contribution of this paper includes three aspects. First, we describe feature interaction among different unit types by fuzzy theory. Second, we develop a new set selection algorithm to represent the complex relation between input and hidden layers in extreme learning machine, in order to enable it to learn different fuzzy integrals. Finally, based on the set selection algorithm, we propose the FELM model for feature interaction description, which has an extremely fast learning speed. Experimental results on artificial benchmarks and real RTS game data show the feasibility and effectiveness of the proposed method in both accuracy and efficiency.

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Acknowledgments

This research project is supported by the HK Polytechnic University Grant 4-ZZAH and the National Natural Science Foundation of China under Grant 61402460.

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Authors and Affiliations

  1. Department of Computing, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong, China

    YingJie Li, Peter H. F. Ng & Simon C. K. Shiu

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  1. YingJie Li
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  2. Peter H. F. Ng
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  3. Simon C. K. Shiu
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Correspondence to YingJie Li.

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Li, Y., Ng, P.H.F. & Shiu, S.C.K. A fast evaluation method for RTS game strategy using fuzzy extreme learning machine. Nat Comput 15, 435–447 (2016). https://doi.org/10.1007/s11047-015-9484-7

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  • DOI: https://doi.org/10.1007/s11047-015-9484-7

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