AI has many techniques and tools at its disposal, yet seems to be lacking some special “juice” needed to create a true being. We propose that the missing ingredients are a general theory of motivation and an operational understanding of natural language. The motivation part comes largely from our animal heritage: a real-world agent must continually respond to external events rather than depend on perfect modeling and planning. The language part, on the other hand, is what makes us human: competent participation in a social group requires one-shot learning and the ability to reason about objects and activities that are not present or on-going. In this paper we propose an architecture for self-motivation, and suggest how a language interpreter can be built on top of such a substrate. With the addition of a method for recording and internalizing dialog, we sketch how this can then be used to impart essential cultural knowledge and behaviors.
By: Jonathan Connell
Published in: RC24377 in 2007
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