A Knowledge Engineer's Comparison of Three Evidence Aggregation Methods
Artificial Intelligence
2013-04-11 v1
Abstract
The comparisons of uncertainty calculi from the last two Uncertainty Workshops have all used theoretical probabilistic accuracy as the sole metric. While mathematical correctness is important, there are other factors which should be considered when developing reasoning systems. These other factors include, among other things, the error in uncertainty measures obtainable for the problem and the effect of this error on the performance of the resulting system.
Cite
@article{arxiv.1304.2744,
title = {A Knowledge Engineer's Comparison of Three Evidence Aggregation Methods},
author = {Donald H. Mitchell and Steven A. Harp and David K. Simkin},
journal= {arXiv preprint arXiv:1304.2744},
year = {2013}
}
Comments
Appears in Proceedings of the Third Conference on Uncertainty in Artificial Intelligence (UAI1987)