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Maximum Entropy Inference for Geographical Information Systems

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Flexible Databases Supporting Imprecision and Uncertainty

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 203))

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Abstract

An immediate problem in approaching GIS (Geographic Information Systems) consists in giving a sufficiently agreed definition of what GIS actually are. For present purposes it seems reasonable to consider GIS as being characterized by a twofold nature. On the one hand, GIS consist of a technology used for certain purposes. From this perspective, the crucial issues in GIS research amount to computing problems, both on the hardware and software level. On the other hand, however, GIS research is increasingly more focussed on theoretical issues concerning the representation of geographic information. According to the latter point of view GIS problems include, at the very least, issues of knowledge representation and reasoning. In this chapter we investigate some of the consequences deriving from approaching GIS from the latter point of view. In particular, we will be insisting on the fact that its s‘conceptual side’, so to speak, commits GIS research to achieving scientific goals which happen to be closely related to some of those pursued in Artificial Intelligence (AI) research.3 In doing so, we adopt a perspective according to which GIS are essentially construed as artificial intelligent agents reasoning about a certain classes of natural environments.

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Author information

Authors and Affiliations

  1. KDDLAB, ISTI-CNR, Pisa, Italy

    Hykel Hosni, Maria Vittoria Masserotti & Chiara Renso

  2. Department of Mathematics, Manchester University, Italy

    Hykel Hosni

Authors
  1. Hykel Hosni
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  2. Maria Vittoria Masserotti
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  3. Chiara Renso
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Editor information

Editors and Affiliations

  1. CNR IDPA, National Research Council of Italy Institute for the Study of Environmental Process Dynamics, via Pasubio 5, Dalmine, BG, I-24044, Italy

    Gloria Bordogna Dr.

  2. Faculty of Engineering, University of Bergamo, viale Marconi 5, Dalmine, BG, I-240044, Italy

    Giuseppe Psaila Professor

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© 2006 Springer

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Hosni, H., Masserotti, M.V., Renso, C. (2006). Maximum Entropy Inference for Geographical Information Systems. In: Bordogna, G., Psaila, G. (eds) Flexible Databases Supporting Imprecision and Uncertainty. Studies in Fuzziness and Soft Computing, vol 203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-33289-8_11

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  • DOI: https://doi.org/10.1007/3-540-33289-8_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33288-6

  • Online ISBN: 978-3-540-33289-3

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