Ploxoma: Testbed for Uncertain Inference
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:49-55, 1995.
This paper compares two formalisms for uncertain inference, Kyburg’s Combinatorial Semantics and Dempster-Shafer belief function theory, on the basis of an example from the domain of medical diagnosis. I review Shafer’s example about the imaginary disease ploxoma and show how it would be represented in Combinatorial Semantics. I conclude that belief function theory has a qualitative advantage because it offers greater flexibility of expression, and provides results about more specific classes of patients. Nevertheless, a quantitative comparison reveals that the inferences sanctioned by Combinatorial Semantics are more reliable than those of belief function theory.