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

Download: PDF

Submission history

[v1] 6 Mar 2010

Unique-IP document downloads: 90 times

Vixra.org is a pre-print repository rather than a journal. Articles hosted may not yet have been verified by peer-review and should be treated as preliminary. In particular, anything that appears to include financial or legal advice or proposed medical treatments should be treated with due caution. Vixra.org will not be responsible for any consequences of actions that result from any form of use of any documents on this website.

Add your own feedback and questions here:
You are equally welcome to be positive or negative about any paper but please be polite. If you are being critical you must mention at least one specific error, otherwise your comment will be deleted as unhelpful.

comments powered by Disqus