when we are less confident in the source. While QBCR2 is more optimistic **and refined; we use it when** we are more confident in the source. Of course, the qualitative conditioning process is less precise than its quantitative counterparts because it is based on a rough approximation, as it normally happens when working with linguistic labels. Such qualitative methods present however some **interests for manipulating information** and beliefs expressed in natural language by human experts and can be helpful for high-level decision support systems.

## References

[1] F. Smarandache and J. Dezert (Editors), "Enrichment of Qualitative Beliefs for Reasoning under Uncertainty", Proceedings of Fusion 2007 International Conference on Information Fusion**, Qu´ebec, Canada,**
July 9-12, 2007.
[2] G. Shafer, "A Mathematical Theory of Evidence", Princeton University Press**, Princeton, NJ, 1976.**

[3] F. Smarandache and J. Dezert (Editors), "Advances and Applications of DSmT for Information Fusion
(Collected works)", American Research Press**, Rehoboth, 2004.**
http://www.gallup.unm.edu/~**smarandache/DSmT-book1.pdf**.

[4] F. Smarandache and J. Dezert (Editors), "Advances and Applications of DSmT for Information Fusion
(Collected works)", Vol.2, American Research Press**, Rehoboth, 2006.**
http://www.gallup.unm.edu/~smarandache/DSmT-book2.pdf.