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Neural Network Approaches to Spatial Knowledge Representation and Inference

In: Intelligent Spatial Decision Support Systems

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  • Yee Leung

    (The Chinese University of Hong Kong)

Abstract

Our discussion so far has concentrated on the symbolic approaches to spatial knowledge representation and inference. Logic (fuzzy and non-fuzzy), production systems, semantic networks, frames, object-oriented programming, and their hybrids all belong to symbolic systems in which knowledge is modeled by symbols. Intelligence is realized by a symbolic structure in which symbols can be manipulated and reasoning can be made. The advantages of the symbolic approaches are that they provide a structured representation of knowledge so that processing elements corresponding to meaningful concepts and inference can be traced and explained. The separation of knowledge from the inference mechanism also makes knowledge update easier and more efficient. The approach is thus a top down approach which gives consensus knowledge to a system by instructing it what to feel and respond without having to gain knowledge through experience. It may be a faster way to build intelligent system.

Suggested Citation

  • Yee Leung, 1997. "Neural Network Approaches to Spatial Knowledge Representation and Inference," Advances in Spatial Science, in: Intelligent Spatial Decision Support Systems, chapter 5, pages 173-227, Springer.
    • Handle: RePEc:spr:adspcp:978-3-642-60714-1_5
    DOI: 10.1007/978-3-642-60714-1_5
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