Presentation | 1998/3/19 A Neural Network Model Which Obtains Active Perception using Reinforcement Learning Mitsuo Takano, Yutaka Sakaguchi, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | We human beings recognize objects through processing only appropriate sensory information dependent on our purpose and context. How do we obtain such active perception function ? To answer this question, the authors tried to construct a neural network model using Temporal Difference (TD) learning, one of the reinforcement algorithms. This model learns how to choose sensory information for getting recognition result efficiently. The result of computer simulation showed that the model obtained such an ability through trial and error. The authors also discussed future approaches to expand the proposed model to a more general model of human recognition mechanism. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | active perception / neural network model / reinforcement learning / TD learning / attention |
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Committee | NC |
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Conference Date | 1998/3/19(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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Registration To | Neurocomputing (NC) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | A Neural Network Model Which Obtains Active Perception using Reinforcement Learning |
Sub Title (in English) | |
Keyword(1) | active perception |
Keyword(2) | neural network model |
Keyword(3) | reinforcement learning |
Keyword(4) | TD learning |
Keyword(5) | attention |
1st Author's Name | Mitsuo Takano |
1st Author's Affiliation | Graduate School of Information Systems, University of Electro-Communications() |
2nd Author's Name | Yutaka Sakaguchi |
2nd Author's Affiliation | Graduate School of Information Systems, University of Electro-Communications |
Date | 1998/3/19 |
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Volume (vol) | vol.97 |
Number (no) | 623 |
Page | pp.pp.- |
#Pages | 6 |
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