2 research outputs found
Credibility Discounting in the Theory of Approximate Reasoning
We are concerned with the problem of introducing credibility type information
into reasoning systems. The concept of credibility allows us to discount
information provided by agents. An important characteristic of this kind of
procedure is that a complete lack of credibility rather than resulting in the
negation of the information provided results in the nullification of the
information provided. We suggest a representational scheme for credibility
qualification in the theory of approximate reasoning. We discuss the concept of
relative credibility. By this idea we mean to indicate situations in which the
credibility of a piece of evidence is determined by its compatibility with
higher priority evidence. This situation leads to structures very much in the
spirit of nonmonotonic reasoning.Comment: Appears in Proceedings of the Sixth Conference on Uncertainty in
Artificial Intelligence (UAI1990
A Combination of Cutset Conditioning with Clique-Tree Propagation in the Pathfinder System
Cutset conditioning and clique-tree propagation are two popular methods for
performing exact probabilistic inference in Bayesian belief networks. Cutset
conditioning is based on decomposition of a subset of network nodes, whereas
clique-tree propagation depends on aggregation of nodes. We describe a means to
combine cutset conditioning and clique- tree propagation in an approach called
aggregation after decomposition (AD). We discuss the application of the AD
method in the Pathfinder system, a medical expert system that offers assistance
with diagnosis in hematopathology.Comment: Appears in Proceedings of the Sixth Conference on Uncertainty in
Artificial Intelligence (UAI1990