Chapter 13 Qualitative Spatial Representation and Reasoning

Abstract

Publisher Summary Early attempts at qualitative spatial reasoning within the qualitative reasoning (QR) community led to the poverty conjecture. The need for spatial representations and spatial reasoning is ubiquitous in artificial intelligence (AI) from robot planning and navigation to interpreting visual inputs to understanding natural language. In all these cases, the need to represent and reason about spatial aspects of the world is of key importance. Related fields of research such as geographic information science (GIScience) have also driven the spatial representation and reasoning community to produce efficient, expressive, and useful calculi. There has been considerable research in spatial representations that are based on metric measurements, in particular within the vision and robotics communities, and also on raster and vector representations in GIScience. This chapter focuses on symbolic and, in particular, qualitative representations. The challenge of qualitative spatial reasoning (QSR) is to provide calculi that allow a machine to represent and reason with spatial entities without resort to the traditional quantitative techniques prevalent in, for example, computer graphics or computer vision communities.