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

Authors:

Simon C. ShiuAuthors Info & Claims

Natural Computing: an international journal, Volume 15, Issue 3

Pages 435 - 447

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

Published: 01 September 2016 Publication History

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.

References

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Cited By

Yang WHuang JHu Y(2019)A Modified Multi-size Convolution Neural Network for Winner Prediction Based on Time Serial DatasetsProceedings of the 2019 4th International Conference on Mathematics and Artificial Intelligence10.1145/3325730.3325744(110-114)Online publication date: 12-Apr-2019
https://dl.acm.org/doi/10.1145/3325730.3325744

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Published In

cover image Natural Computing: an international journal

Natural Computing: an international journal Volume 15, Issue 3

September 2016

160 pages

ISSN:1567-7818

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Copyright © Copyright © 2016 Springer Science+Business Media Dordrecht.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 September 2016

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Cited By

Yang WHuang JHu Y(2019)A Modified Multi-size Convolution Neural Network for Winner Prediction Based on Time Serial DatasetsProceedings of the 2019 4th International Conference on Mathematics and Artificial Intelligence10.1145/3325730.3325744(110-114)Online publication date: 12-Apr-2019
https://dl.acm.org/doi/10.1145/3325730.3325744

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