Abstract
In order to collaborate with people in the real world, cognitive systems must be able to represent and reason about spatial regions 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 geometry alone. Instead it is defined by its functional use, implied by nearby objects and their configuration. In this paper, we define such areas as context-dependent spatial regions and present a cognitive system able to learn them by combining qualitative spatial representations, semantic labels, and analogy. The system is capable of generating a collection of qualitative spatial representations describing the configuration 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 demonstrate 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.
Original language | English |
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Title of host publication | Proceedings of the National Conference on Artificial Intelligence |
Publisher | Association for the Advancement of Artificial Intelligence |
Pages | 200-206 |
Number of pages | 7 |
Volume | 1 |
ISBN (Print) | 9781577355687 |
Publication status | Published - 1 Jan 2012 |
Event | 26th AAAI Conference on Artificial Intelligence - Toronto, Canada Duration: 22 Jul 2012 → 26 Jul 2012 |
Conference
Conference | 26th AAAI Conference on Artificial Intelligence |
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Country/Territory | Canada |
City | Toronto |
Period | 22/07/12 → 26/07/12 |