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Symbolic Approaches to Spatial Knowledge Representation and Inference

In: Intelligent Spatial Decision Support Systems

Author

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  • Yee Leung

    (The Chinese University of Hong Kong)

Abstract

Knowledge representation and inference are main concerns in building systems with artificial intelligence. To be able to understand and to reason, an intelligent machine needs prior knowledge about the problem domain. To understand sentences, for example, natural language understanding systems have to be equipped with prior knowledge about topics of conversation and participants. To be able to see and interpret scenes, vision systems need to have in store prior information of objects to be seen. Therefore, any intelligent systems should possess a knowledge base containing facts and concepts related to a problem domain and their relationships. There should also be an inference mechanism which can process symbols in the knowledge base and derive implicit knowledge from explicitly expressed knowledge.

Suggested Citation

  • Yee Leung, 1997. "Symbolic Approaches to Spatial Knowledge Representation and Inference," Advances in Spatial Science, in: Intelligent Spatial Decision Support Systems, chapter 2, pages 11-57, Springer.
    • Handle: RePEc:spr:adspcp:978-3-642-60714-1_2
    DOI: 10.1007/978-3-642-60714-1_2
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