Tracking Regions using Conceptual Neighbourhoods

The use of conceptual neighbourhood diagrams (CNDs) is now fairly well established in AI literature. In AI applications, and in particular Qualitative Spatial Reasoning (QSR), the nodes of CNDs are typically populated by individual relations defined on regions or intervals, and the edges connecting adjacent nodes as continuous transformations between them. While CNDs have been used to measure the similarity between relations and their relata, region-identity is often assumed rather than explicitly determined by the representational theory used. In this paper we explore QSR theories and relation-based measures of similarity for regionidentity in a dynamic setting. Ambiguity in potential identity mappings arising from using a weak qualitative similarity measure is reduced by combining conceptually related but distinct QSR theories, each supporting a CND. Results from a simulation program that implements the theory are discussed.