Titlebook: Intelligent Spatial Decision Support Systems; Yee Leung Book 1997 Springer-Verlag Berlin Heidelberg 1997 Artifical Intelligence.Import.alg

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Spatial Data Models and Data Structures,Burrough, 1986; Vaughaneia .., 1988; Maguire .., 1991; Laurini and Thompson, 1992). The purpose of this chapter is not to provide an overview of research in data models and data structures in general and GIS in particular. I concentrate instead on the modeling and structuring of geometric and attribute data, especially under uncertainty, in SDSS.

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Conclusion,ne interaction. It should be able to assist decision making by providing alternatives through the efficient processing of knowledge and information in a complex environment. The system should also be robust and flexible in managing uncertainty of various types. Furthermore, it should be responsive to users’ demands.

秘密會議

Neural Network Approaches to Spatial Knowledge Representation and Inference,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.

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Introduction, clear-cut solution procedures, or even semi-structured problems for which combinations of standard procedures and individual judgements have to be used to find a solution. All these processes generally involve voluminous spatial and aspatial information, structured and unstructured knowledge, human valuation and judgement.

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OWL

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,Knowledge Acquisition for Spatial Inference — The Case of Genetic Algorithms,dation, a good system design, and a powerful and user-friendly software environment, the success of intelligent SDSS lies heavily on the domain specific knowledge they acquired. Knowledge acquisition thus plays a very important role in the design of intelligent SDSS in general and spatial expert systems in particular (Barbanente .., 1992).

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An Expert System Shell for Building Spatial-Decision-Support-System Development Tool,lding SDSS to solve specific spatial problems in an effective and efficient manner. The overall objective is to have a system which can materialize the general SDSS architecture depicted in Fig. 1.2 in Chapter 1.

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