Generating belief functions from qualitative preferences: An approach to eliciting expert judgments and deriving probability functions

Abstract

It has long been recognized that the capability of using qualitative preferences to generate numeric judgments in expert systems and intelligent decision support systems (ES/IDSS) is essential. Although qualitative preferences and expressions facilitate communication and are useful for thinking about complex problems there is no simple and straightforward way to transform them for computer processing. Thus, the developer of the ES/IDSS must work with each expert to transform his/her vague and incomplete preferences into numeric estimates. This is a very difficult task and few techniques are available to assist developers with it. In this paper we present a qualitative discriminant process (QDP) for eliciting qualitative preferences from experts and generating appropriate numerical representations, as required by ES/IDSS, that utilize the Dempster-Shafer Theory. This approach provides a strategy for generating consistent numeric values for belief functions from qualitative preferences that can be used with the Dempster rules. We illustrate the approach with a case example.