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Titlebook: Deep Fusion of Computational and Symbolic Processing; Takeshi Furuhashi,Shun’Ichi Tano,Hans-Arno Jacobse Book 2001 Springer-Verlag Berlin

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發(fā)表于 2025-3-21 17:34:38 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Deep Fusion of Computational and Symbolic Processing
編輯Takeshi Furuhashi,Shun’Ichi Tano,Hans-Arno Jacobse
視頻videohttp://file.papertrans.cn/265/264552/264552.mp4
概述First publication of recent results of study under the name of integration of computational processing and symbolic processing.Thorough coverage of recent attempts to combine/hybridize/fuse symbolic p
叢書(shū)名稱Studies in Fuzziness and Soft Computing
圖書(shū)封面Titlebook: Deep Fusion of Computational and Symbolic Processing; Takeshi Furuhashi,Shun’Ichi Tano,Hans-Arno Jacobse Book 2001 Springer-Verlag Berlin
描述Symbolic processing has limitations highlighted by the symbol grounding problem. Computational processing methods, like fuzzy logic, neural networks, and statistical methods have appeared to overcome these problems. However, they also suffer from drawbacks in that, for example, multi-stage inference is difficult to implement. Deep fusion of symbolic and computational processing is expected to open a new paradigm for intelligent systems. Symbolic processing and computational processing should interact at all abstract or computational levels. For this undertaking, attempts to combine, hybridize, and fuse these processing methods should be thoroughly investigated and the direction of novel fusion approaches should be clarified. This book contains the current status of this attempt and also discusses future directions.
出版日期Book 2001
關(guān)鍵詞Computational Processing; Neuro-symbolic System; Symbolic Processing; control; dynamical systems; fuzzy l
版次1
doihttps://doi.org/10.1007/978-3-7908-1837-6
isbn_softcover978-3-662-00373-2
isbn_ebook978-3-7908-1837-6Series ISSN 1434-9922 Series E-ISSN 1860-0808
issn_series 1434-9922
copyrightSpringer-Verlag Berlin Heidelberg 2001
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978-3-662-00373-2Springer-Verlag Berlin Heidelberg 2001
板凳
發(fā)表于 2025-3-22 00:42:05 | 只看該作者
https://doi.org/10.1007/978-1-4842-3603-1st (symbolic) and distributed representations, based on the two-level approach proposed in Sun (1995). The model learns and utilizes procedural and declarative knowledge, tapping into the synergy of the two types of processes. It unifies neural, reinforcement, and symbolic methods to perform on-line
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https://doi.org/10.1007/978-1-4842-3603-1se methods is studied. A special processor to combine different methods is necessary for integration. It is called an integrator. Among various information-processing methods, only declarative knowledge-based method is suited for an integrator. Then the realistic way of developing the integrator is
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https://doi.org/10.1007/978-1-4842-3603-1 “fuzzy” sequential knowledge for the description of dynamic characteristics of a system. Symbolic Dynamic System(SDS), a model for symbolic sequences, is extended to deal with “fuzzy” symbolic sequences. This approach introduces topological nature into the symbolic sequences, which allows an interp
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https://doi.org/10.1007/978-1-4842-3603-1 comparison with its predecessor because the learning and the knowledge extraction process are faster and are accomplished in an incremental way . INSS offers a new approach applicable to constructive machine learning with high-performance tools, even in the presence of incomplete or erroneous data.
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