ASEMIQUAUTATIVE APPROACH TO REASONING IN PROBABILISTIC NETWORKS
Simon Parsons, Mirko Dohnal · Applied Artificial Intelligence · 1993
This paper proposes the use of semiqualitative modeling for reasoning in probabilistic networks. Semiqualitative modeling is a generalization of qualitative modeling that refines the set of intervals in which values may be expressed. The advantage of semiqualitative modeling of probabilistic reasoning over more traditional methods is that a semiqualitative model can cope with incomplete and imprecise information that would prevent a more traditional model from functioning. The semi-qualitative analysis of a well-known example from the literature is presented, and conclusions about the general use of semiqualitative modeling in reasoning under uncertainty is discussed.