Plausible Reasoning from Spatial Observations
Abstract
This article deals with plausible reasoning from incomplete knowledge about large-scale spatial properties. The available information, consisting of a set of pointwise observations, is extrapolated to neighbour points. We use belief functions to represent the influence of the knowledge at a given point to another point; the quantitative strength of this influence decreases when the distance between both points increases. These influences are aggregated using a variant of Dempster's rule of combination taking into account the relative dependence between observations.
Cite
Text
Lang and Muller. "Plausible Reasoning from Spatial Observations." Conference on Uncertainty in Artificial Intelligence, 2001.Markdown
[Lang and Muller. "Plausible Reasoning from Spatial Observations." Conference on Uncertainty in Artificial Intelligence, 2001.](https://mlanthology.org/uai/2001/lang2001uai-plausible/)BibTeX
@inproceedings{lang2001uai-plausible,
title = {{Plausible Reasoning from Spatial Observations}},
author = {Lang, Jérôme and Muller, Philippe},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2001},
pages = {285-292},
url = {https://mlanthology.org/uai/2001/lang2001uai-plausible/}
}