Ploxoma: Testbed for Uncertain Inference

Hannah Blau
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:49-55, 1995.

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 this Paper

BibTeX
@InProceedings{pmlr-vR0-blau95a, title = {Ploxoma: Testbed for Uncertain Inference}, author = {Blau, Hannah}, booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics}, pages = {49--55}, year = {1995}, editor = {Fisher, Doug and Lenz, Hans-Joachim}, volume = {R0}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/r0/blau95a/blau95a.pdf}, url = {https://proceedings.mlr.press/r0/blau95a.html}, 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.}, note = {Reissued by PMLR on 01 May 2022.} }
Endnote
%0 Conference Paper %T Ploxoma: Testbed for Uncertain Inference %A Hannah Blau %B Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1995 %E Doug Fisher %E Hans-Joachim Lenz %F pmlr-vR0-blau95a %I PMLR %P 49--55 %U https://proceedings.mlr.press/r0/blau95a.html %V R0 %X 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. %Z Reissued by PMLR on 01 May 2022.
APA
Blau, H.. (1995). Ploxoma: Testbed for Uncertain Inference. Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R0:49-55 Available from https://proceedings.mlr.press/r0/blau95a.html. Reissued by PMLR on 01 May 2022.

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