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Natural Computing
Published in: Natural Computing 3/2016

01-09-2016

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

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

Published in: Natural Computing | Issue 3/2016

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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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Metadata
Title
A fast evaluation method for RTS game strategy using fuzzy extreme learning machine
Authors
YingJie Li
Peter H. F. Ng
Simon C. K. Shiu
Publication date
01-09-2016
Publisher
Springer Netherlands
Published in
Natural Computing / Issue 3/2016
Print ISSN: 1567-7818
Electronic ISSN: 1572-9796
DOI
https://doi.org/10.1007/s11047-015-9484-7

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