Presentation 1998/9/24
Time Dependent Classifier System
Masahiro FUKUYORI, Akira HARA, Tomoharu NAGAO,
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Abstract(in English) In this paper, Time-dependent Classifier System ; TCS is proposed to solve the perceptual aliasing in reinforcement learning as learning proceeds. In TCS, it keeps the constant length of time sequential information, and uses them only when it is necessary to solve the perceptual aliasing. So, the changeable lengths of rules are proposed in TCS using the time tags. And the internal action to detect the information before 1 time-step is used to link the present information to the past information. And the learning method is considered because it is complicated and difficult to learn by TCS. And the several maze problems with partially observable Markov decision processes are experimented to verify TCS's effectiveness.
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Keyword(in English) Classifier System / Reinforcement Learning / Autonomous Agent
Paper # AI98-38
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Conference Information
Committee AI
Conference Date 1998/9/24(1days)
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Registration To Artificial Intelligence and Knowledge-Based Processing (AI)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Time Dependent Classifier System
Sub Title (in English)
Keyword(1) Classifier System
Keyword(2) Reinforcement Learning
Keyword(3) Autonomous Agent
1st Author's Name Masahiro FUKUYORI
1st Author's Affiliation Imaging Science and Engineering Laboratory, Tokyo Institute of Technology()
2nd Author's Name Akira HARA
2nd Author's Affiliation Imaging Science and Engineering Laboratory, Tokyo Institute of Technology
3rd Author's Name Tomoharu NAGAO
3rd Author's Affiliation Imaging Science and Engineering Laboratory, Tokyo Institute of Technology
Date 1998/9/24
Paper # AI98-38
Volume (vol) vol.98
Number (no) 296
Page pp.pp.-
#Pages 8
Date of Issue