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

Ying Jie Li, Peter H.F. Ng, Chi Keung Simon Shiu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

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.
Original languageEnglish
Pages (from-to)435-447
Number of pages13
JournalNatural Computing
Volume15
Issue number3
DOIs
Publication statusPublished - 1 Sep 2016

Keywords

  • Extreme learning machine
  • Feature interaction
  • Fuzzy integral
  • Real time strategy game
  • Strategy evaluation

ASJC Scopus subject areas

  • Computer Science Applications

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