Deep Fusion of Computational and Symbolic Processing by Takeshi FuruhashiDeep Fusion of Computational and Symbolic Processing by Takeshi Furuhashi

Deep Fusion of Computational and Symbolic Processing

byTakeshi FuruhashiEditorShun'Ichi Tano, Hans-Arno Jacobsen

Paperback | July 28, 2012

Pricing and Purchase Info

$133.20 online 
$151.95 list price save 12%
Earn 666 plum® points

Prices and offers may vary in store


In stock online

Ships free on orders over $25

Not available in stores


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.
Title:Deep Fusion of Computational and Symbolic ProcessingFormat:PaperbackDimensions:256 pagesPublished:July 28, 2012Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:3662003732

ISBN - 13:9783662003732

Look for similar items by category:


Table of Contents

L.A. Zadeh: Foreword.- T. Furuhashi, S. Tano, H.-A. Jacobsen: Introduction.- Integration of Computational and Symbolic Processing: R. Sun, T. Peterson: A Subsymbolic and Symbolic Model for Learning Sequential Decision Tasks; S. Ohsuga: Integration of Different Information Processing Methods; H. Tsukimoto: Symbol Pattern Integration Using Multilinear Functions.- Toward Deep Fusion of Computational and Symbolic Processing: R. Schonknecht, M. Spott, M. Riedmiller: Design of Autonomously Learning Controllers Using FYNESSE; I. Takeuchi, T. Furuhashi: Modeling for Dynamical Systems with Fuzzy Sequential Knowledge; F. Osorio, B. Amy, A. Cechin: Hybrid Machine Learning Tools: INSS - A Neuro-Symbolic System for Constructive Machine Learning; H.-A. Jacobsen: A Generic Architecture for Hybrid Intelligence Systems; S. Tano: New Paradigm toward Deep Fusion of Computational and Symbolig Processing.- Knowledge Representation: T. Takagai: Fusion of Symbolic and Quantitative Processing by Conceptual Fuzzy Sets; M. Hagiwara, N. Ikeda: Novel Knowledge Representation (Area Representation) and the Implementation by Neural Network; T. Mukai, T. Nishimura, T. Endo, R. Oka: A Symbol Ground Problem of Gesture Motion through a Self-organizing Network of Time-varying Motion Images.