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Additional info for Advances in Pattern Recognition: Proceedings of the Sixth International Conference, Indian Statistical Institute, Kolkata, India 2-4 January 2007
Stock (Kluwer, 1997) pp. 5-41. S. Dutta, International Journal of Approximate Reasoning 5, 307 (1991). L. A. Zadeh, Information Sciences 8, 199 (1975). D. Dubois and H. Prade, Fuzzy Sets and Systems: Theory and Applications (Academic Press, NewYork, 1980). D. Dubois and H. Prade, Information Sciences 36, 85 (1985). D. Dubois, H. Prade and R. Yager, Merging Fuzzy Information, in Handbook of Fuzzy Sets Series, Approximate Reasoning and Information Systems, eds. J. Bezdek, D. Dubois and H. Prade (Kluwer, 1999) I.
Fuzzy set theory finds in spatial information pro- 20 On the Interest of Spatial Relations and Fuzzy Representations cessing a growing application domain. ) together with expert knowledge, but also by the large and powerful toolbox it offers for dealing with spatial information under imprecision. This is in particular highlighted when spatial structures or objects are directly represented by fuzzy sets. If even less information is available, we may have to reason about space in a purely qualitative way, and the symbolic setting is then more appropriate.
Lu, D. Kang and P. Wang, A family of extended fuzzy description logics, in 29th Annual International Computer Software and Applications Conference (COMPSAC'05), (IEEE Computer Society, Los Alamitos, CA, USA, 2005). G. Stoilos, G. Stamou and J. Pan, Handling imprecise knowledge with fuzzy description logic, in International Workshop on Description Logics (DL 06), (Lake District, UK, 2006). U. Straccia, Description logics with fuzzy concrete domains, in 21st Conference on Uncertainty in Artificial Intelligence (UAI-05), eds.