Towards a cognitive system that can recognize spatial regions based on context

In order to collaborate with people in the real world, cogni-tive systems must be able to represent and reason about spa-tial regions in human environments. Consider the command “go to the front of the classroom”. The spatial region men-tioned (the front of the classroom) is not perceivable using geometry alone. Instead it is defined by its functional use, implied by nearby objects and their configuration. In this pa-per, we define such areas as context-dependent spatial regions and present a cognitive 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. To evaluate this process we compare detected regions to annotations made on maps of real rooms by human volunteers.