Using Anchor Points to Define and Transfer Spatial Regions Based on Context

In order to collaborate with people in the real world, AI sys-tems must be able to represent and reason about spatial re-gions in human environments. Consider the command “go to the front of the classroom”. The spatial region mentioned (the front of the classroom) is not perceivable using geom-etry alone. Instead it is defined by its functional use, im-plied by nearby objects and their configuration. In this pa-per, we define such areas as context-dependent spatial re-gions and present a system able to learn them by combin-ing qualitative spatial representations, semantic labels, and analogy. The system is capable of generating a collection of qualitative spatial representations describing the configu-ration of the entities it perceives in the world. It can then be taught context-dependent spatial regions using anchor points defined on these representations. From this we then demon-strate how an existing computational model of analogy can be used to detect context-dependent spatial regions in previously unseen rooms by transferring the necessary anchor points. To evaluate this process we compare detected regions to annota-tions made on maps of real rooms by human volunteers.