Deep Fusion of Computational and Symbolic Processingby Takeshi Furuhashi
Pub. Date: 01/25/2001
Publisher: Physica-Verlag HD
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… See more details below
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.
Table of ContentsL.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.
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