Plausible reasoning from spatial observations
This addresses spatial reasoning under uncertainty for domains like geography or robotics, but it appears incremental as it builds on existing belief function methods.
The paper tackles the problem of plausible reasoning from incomplete spatial observations by extrapolating pointwise data to neighbor points using belief functions, where influence decreases with distance, and aggregates these with a variant of Dempster's rule that accounts for relative dependence.
This article deals with plausible reasoning from incomplete knowledge about large-scale spatial properties. The availableinformation, consisting of a set of pointwise observations,is extrapolated to neighbour points. We make use of 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 arethen aggregated using a variant of Dempster's rule of combination which takes into account the relative dependence between observations.