On reasoning in networks with qualitative uncertainty
This work addresses the challenge of integrating multiple uncertainty formalisms for researchers in AI and reasoning, but it is incremental as it builds on existing qualitative methods.
The paper tackles the problem of qualitative reasoning under uncertainty by proposing a method applicable to probabilistic, possibilistic, and Dempster-Shafer evidence theories, and demonstrates that qualitative integration of these formalisms is possible under certain assumptions through analysis of simple networks.
In this paper some initial work towards a new approach to qualitative reasoning under uncertainty is presented. This method is not only applicable to qualitative probabilistic reasoning, as is the case with other methods, but also allows the qualitative propagation within networks of values based upon possibility theory and Dempster-Shafer evidence theory. The method is applied to two simple networks from which a large class of directed graphs may be constructed. The results of this analysis are used to compare the qualitative behaviour of the three major quantitative uncertainty handling formalisms, and to demonstrate that the qualitative integration of the formalisms is possible under certain assumptions.