Abstract
The computational processing methods appeared to overcome the drawbacks of the symbolic processing method. Recently the serious limitations of the computational processing methods have been found through the development of the highly intelligent user interface systems. The simple solutions to the limitations are the return to the symbolism or the pursue of the hierarchical system architecture. However it is indispensable to deeply combine the symbolic and computational processing in order to realize the highly intelligent system. This paper analyzes the leading models, which use symbolic and computational processing, and clarifies the problems of them. The level of the combination is not deep, high and wide enough. Based on the analysis, we propose a new paradigm toward deep fusion of computational and symbolic processing and show the new model as the first step of the paradigm. The model is realized by “Symbol Emergence Method for Q-Learning Neural Network”. We testified the validity of the new method.
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© 2001 Physica-Verlag Heidelberg
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Tano, S. (2001). New Paradigm toward Deep Fusion of Computational and Symbolic Processing. In: Furuhashi, T., Tano, S., Jacobsen, HA. (eds) Deep Fusion of Computational and Symbolic Processing. Studies in Fuzziness and Soft Computing, vol 59. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1837-6_8
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DOI: https://doi.org/10.1007/978-3-7908-1837-6_8
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-662-00373-2
Online ISBN: 978-3-7908-1837-6
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