Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/7714
Title: A fast evaluation method for RTS game strategy using fuzzy extreme learning machine
Authors: Li, Y
Ng, PHF
Shiu, SCK 
Keywords: Extreme learning machine
Feature interaction
Fuzzy integral
Real time strategy game
Strategy evaluation
Issue Date: 2015
Publisher: Kluwer Academic Publishers
Source: Natural computing, 2015 How to cite?
Journal: Natural Computing 
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.
URI: http://hdl.handle.net/10397/7714
ISSN: 1567-7818
DOI: 10.1007/s11047-015-9484-7
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

38
Last Week
2
Last month
Checked on Aug 13, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.