Spatial Representation and Reasoning

A robot moving through an environment, an interface to a geographical database, and a natural language program for giving directions all need means for representing and reasoning about spatial properties and relations. These include shape, size, distance, orientation, and relative location. The most precise and highly developed system of spatial representation is the mathematics of Euclidean space. But very often this is too precise for the purposes at hand. We may not have exact information and we may not need precise answers. For example, if we are telling someone how to get to the post office, we don’t need to be more precise than the streets to follow. For this reason, much work in artificial intelligence has focused on qualitative spatial representation and reasoning. Indeed, studies of human problem solving and language understanding [Talmy 1983, Herskovits 1986, Langacker, 1987, Lakoff, 1987, Tversky 1993, Landau & Jackendoff 1993] indicate that we draw fairly subtle and impor-