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2005 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT'05)
Fuzzy Q-Learning with the Modified ART Neural Network
Compi?gne University of Technology, France
September 19-September 22
ISBN: 0-7695-2416-8
ASCII Text
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Hirokae Oeda, Noaki Hanada, Hideake Kimoto, Takeshi Naraki, Kenichi Takahashi, Tetsuhiro Miyahara, "Fuzzy Q-Learning with the Modified ART Neural Network," Intelligent Agent Technology, IEEE / WIC / ACM International Conference on, pp. 308-305, 2005 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT'05), 2005.
 
 
BibTex
x
 
@article{ 10.1109/IAT.2005.78,
author = {Hirokae Oeda and Noaki Hanada and Hideake Kimoto and Takeshi Naraki and Kenichi Takahashi and Tetsuhiro Miyahara},
title = {Fuzzy Q-Learning with the Modified ART Neural Network},
journal ={Intelligent Agent Technology, IEEE / WIC / ACM International Conference on},
volume = {0},
year = {2005},
isbn = {0-7695-2416-8},
pages = {308-305},
doi = {http://doi.ieeecomputersociety.org/10.1109/IAT.2005.78},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Intelligent Agent Technology, IEEE / WIC / ACM International Conference on
TI - Fuzzy Q-Learning with the Modified ART Neural Network
SN - 0-7695-2416-8
SP308
EP305
A1 - Hirokae Oeda,
A1 - Noaki Hanada,
A1 - Hideake Kimoto,
A1 - Takeshi Naraki,
A1 - Kenichi Takahashi,
A1 - Tetsuhiro Miyahara,
PY - 2005
KW - null
VL - 0
JA - Intelligent Agent Technology, IEEE / WIC / ACM International Conference on
ER -
 
Hirokae Oeda, Department of Intelligent Systems, Hiroshima City University, Hiroshima, Japan
Noaki Hanada, Department of Intelligent Systems, Hiroshima City University, Hiroshima, Japan
Hideake Kimoto, Department of Intelligent Systems, Hiroshima City University, Hiroshima, Japan
Takeshi Naraki, Department of Intelligent Systems, Hiroshima City University, Hiroshima, Japan
Kenichi Takahashi, Department of Intelligent Systems, Hiroshima City University, Hiroshima, Japan
Tetsuhiro Miyahara, Department of Intelligent Systems, Hiroshima City University, Hiroshima, Japan

We present a methiod to acquire rules for agent behavior, where continues numeric percepts are classified into categories by fuzzy ART and fuzzy Q-Learning is employed to acquire rules. To make fuzzy such that it slects some categories for a percept vector and returns them with their fitness values. For efficient learning, we also present method that integrates two ctaegories into one, where we define the similarity for any category pair and it is utilized for integration. Moreover, a vigilance parameter is defined for all categories. The methods and some experiments have been done.

Citation:
Hirokae Oeda, Noaki Hanada, Hideake Kimoto, Takeshi Naraki, Kenichi Takahashi, Tetsuhiro Miyahara, "Fuzzy Q-Learning with the Modified ART Neural Network," iat, pp.308-305, 2005 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT'05), 2005
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