Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/64337
Title: Rapid game strategy evaluation using fuzzy extreme learning machine
Authors: Li, Y
Ng, PHF
Shiu, SCK 
Issue Date: 2013
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), v. 8251, p. 250-255 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: Interactions among game units can be conveniently described by fuzzy measures and integrals. Focusing on Warcraft, there are several good results of unit selection strategy evaluation for a genetic algorithm that search in plan space. However, this kind of evaluators are suffered from high complexity in fuzzy measure determination. In this paper, we novelly combine Extreme Learning Machine(ELM) and Fuzzy Integral(FI) to achieve a fast evaluation of game strategy. Experimental comparison demonstrates the effectiveness of the proposed method in both time and accuracy.
Description: 5th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2013, Kolkata, India, December 10-14, 2013
URI: http://hdl.handle.net/10397/64337
ISBN: 978-3-642-45061-7 (print)
978-3-642-45062-4 (online)
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-642-45062-4_34
Appears in Collections:Conference Paper

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