Presentation 2005/6/17
Distributed-representation-based reasoning by the trajectory attractor model
Ken YAMANE, Atsuo SUEMITSU, Masahiko MORITA,
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Abstract(in English) The trajectory attractor model is a recurrent neural network that can make continuous state transitions along learned trajectories, and is capable of simulating any large-scale finite automaton without using local representations or symbols. In the present paper, we show a system using this model that reasons based on distributed representation. This model not only has strong generalization ability but also can deal with exceptional data in a simple and natural manner, and thus can make common-sense inferences from a limited amount of given rules.
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Keyword(in English) nonmonotone neural network / selective desensitization / nonmonotonic reasoning / artificial intelligence
Paper # NC2005-24
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Committee NC
Conference Date 2005/6/17(1days)
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Registration To Neurocomputing (NC)
Language JPN
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Title (in English) Distributed-representation-based reasoning by the trajectory attractor model
Sub Title (in English)
Keyword(1) nonmonotone neural network
Keyword(2) selective desensitization
Keyword(3) nonmonotonic reasoning
Keyword(4) artificial intelligence
1st Author's Name Ken YAMANE
1st Author's Affiliation Graduate School of Systems and Information Engineering, University of Tsukuba()
2nd Author's Name Atsuo SUEMITSU
2nd Author's Affiliation Graduate School of Systems and Information Engineering, University of Tsukuba
3rd Author's Name Masahiko MORITA
3rd Author's Affiliation Graduate School of Systems and Information Engineering, University of Tsukuba
Date 2005/6/17
Paper # NC2005-24
Volume (vol) vol.105
Number (no) 131
Page pp.pp.-
#Pages 6
Date of Issue