Ploxoma: Testbed for Uncertain Inference
Abstract
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.
Cite
Text
Blau. "Ploxoma: Testbed for Uncertain Inference." Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, 1995.Markdown
[Blau. "Ploxoma: Testbed for Uncertain Inference." Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, 1995.](https://mlanthology.org/aistats/1995/blau1995aistats-ploxoma/)BibTeX
@inproceedings{blau1995aistats-ploxoma,
title = {{Ploxoma: Testbed for Uncertain Inference}},
author = {Blau, Hannah},
booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics},
year = {1995},
pages = {49-55},
volume = {R0},
url = {https://mlanthology.org/aistats/1995/blau1995aistats-ploxoma/}
}