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Learning with Deictic Representations

The Problem: Humans speak, and probably think, of the world as being made up of objects. Individual objects have features, and sets of objects stand in various relations to one another. If artificial agents are to successfully interact with our world, they will need to represent and reason about objects, their features, and relations that hold among them. We are interested in how agents embedded in inherently relational environments, such as physically embodied robots, can learn to act.