Modifiable Combining Functions
Artificial Intelligence
2013-04-11 v1
Abstract
Modifiable combining functions are a synthesis of two common approaches to combining evidence. They offer many of the advantages of these approaches and avoid some disadvantages. Because they facilitate the acquisition, representation, explanation, and modification of knowledge about combinations of evidence, they are proposed as a tool for knowledge engineers who build systems that reason under uncertainty, not as a normative theory of evidence.
Cite
@article{arxiv.1304.2712,
title = {Modifiable Combining Functions},
author = {Paul Cohen and Glenn Shafer and Prakash P. Shenoy},
journal= {arXiv preprint arXiv:1304.2712},
year = {2013}
}
Comments
Appears in Proceedings of the Third Conference on Uncertainty in Artificial Intelligence (UAI1987)