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.

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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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