Presentation 2010-12-21
Spoken Dialogue Manager Using Partially Observable Markov Decision Processes with Hierarchical Reinforcement Learning
Yasuhide KISHIMOTO, Tetsuya TAKIGUCHI, Yasuo Ariki,
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Abstract(in English) In this paper, we propose a dialogue manager using Partially Observable Markov Decision Processes (POMDP) that can treat ambiguous information. It can recover from the recognition error while naturally dialogue. However, a dialogue manager produced with a POMDP has been limited to artificial small tasks because calculation of reinforcement learning becomes complicated as the number of state variables increases. We also propose a variation of the classic POMDP incorporating hierarchical reinforcement learning. It can deal with large task than traditional system. The results show the effectiveness of proposed method.
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Keyword(in English) spoken dialogue system / Partially Observable Markov Decision Processes / hierarchical reinforcement learning
Paper # NLC2010-25,SP2010-98
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Committee NLC
Conference Date 2010/12/13(1days)
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Registration To Natural Language Understanding and Models of Communication (NLC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Spoken Dialogue Manager Using Partially Observable Markov Decision Processes with Hierarchical Reinforcement Learning
Sub Title (in English)
Keyword(1) spoken dialogue system
Keyword(2) Partially Observable Markov Decision Processes
Keyword(3) hierarchical reinforcement learning
1st Author's Name Yasuhide KISHIMOTO
1st Author's Affiliation Graduate School of Engineering, Kobe University()
2nd Author's Name Tetsuya TAKIGUCHI
2nd Author's Affiliation Organization of Advanced Science and Technology, Kobe University
3rd Author's Name Yasuo Ariki
3rd Author's Affiliation Organization of Advanced Science and Technology, Kobe University
Date 2010-12-21
Paper # NLC2010-25,SP2010-98
Volume (vol) vol.110
Number (no) 356
Page pp.pp.-
#Pages 6
Date of Issue