Artificial Intelligence

   

DSmT Qualitative Reasoning Based on 2-Tuple Linguistic Representation Model

Authors: Xin-De Li, Xian-Zhong Dai, Jean Dezert, Florentin Smarandache

Most of modern systems for information retrieval, fusion and management have to deal more and more with information expressed quatitatively (by linguistic labels) since human reports are better and easier expressed in natural language than with numbers. In this paper, we propose to use Herrera-Martínez' 2-Tuple linguistic representation model (i.e. equidistant linguistic labels with a numeric value assessment) for reasoning with uncertain and qualitative information in Dezert-Smarandache Theory (DSmT) framework to preserve the precision and the efficiency of the fusion of linguistic information expressing the expert's qualitative beliefs. We present operators to deal with the 2-Tuples and show from a simple example how qualitative DSmT-based fusion rules can be used for qualitative reasoning and fusioning under uncertainty.

Comments: 6 pages

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Submission history

[v1] 6 Mar 2010

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