Survey of several forms of updating, with a practical illustrative example. We study several updating (conditioning) schemes that emerge naturally from a common scenarion to provide some insights into their meaning. Updating is a subtle operation and there is no single method, no single 'good' rule. The choice of the appropriate rule must always be given due consideration. Planchet (1989) presents a mathematical survey of many rules. We focus on the practical meaning of these rules. After summarizing the several rules for conditioning, we present an illustrative example in which the various forms of conditioning can be explained.

This research has been supported by Air Force Research Laboratory, Rome, NY, USA, in June and July 2009. Florentin Smarandache, Mark Alford Air Force Research Laboratory, RIEA, 525 Brooks Rd., Rome, NY 13441-4505, USA Abstract: In this paper we introduce two new DSm fusion conditioning rules with example, and as a generalization of them a class of DSm fu sion conditioning rules, and then extend them to a class of DSm conditioning rules. Keywords: conditional fusion rules, Dempster's conditioning rule, Dezert-Smarandache Theory, DSm conditioning rules 0. Introduction In order to understand the material in this paper, it is first necessary to define the terms that we'll be using: - Frame of discernment th e set of all hypotheses. This research has been supported by Air Force Research Laboratory, Rome, NY, USA, in June and July 2009. In the case when their intersection is empty, we consider these hypotheses disjoint.}

In this paper, we propose a simple arithmetic of linguistic labels which allows a direct extension of quantitative Belief Conditioning Rules (BCR) proposed in the DSmT [3, 4] framework to their qualitative counterpart. Qualitative beliefs assignments are well adapted for manipulated information expressed in natural language and usually reported by human expert or AIbased expert systems. A new method for computing directly with words (CW) for combining and conditioning qualitative information is presented. CW, more precisely computing with linguistic labels, is usually more vague, less precise than computing with numbers, but it is expected to offer a better robustness and flexibility for combining uncertain and conflicting human reports than computing with numbers because in most of cases human experts are less efficient to provide (and to justify) precise quantitative beliefs than qualitative beliefs. Before extending the quantitative DSmT-based conditioning rules to their qualitative counterparts, it will be necessary to define few but new important operators on linguistic labels and what is a qualitative belief assignment. Then we will show though simple examples how the combination of qualitative beliefs can be obtained in the DSmT framework.