Learning the structure of activities for a mobile robot

At birth, the human infant has only a very rudimentary perceptual system and similarly rudimentary control over its musculature. As time goes on, a child develops. Its ability to control, perceive, and predict its own behavior improves as it interacts with its environment. We are interested in the process of development, in particular with respect to activity. How might an intelligent agent of our own design learn to represent and organize procedural knowledge so that over time it becomes more competent at its achieving goals in its own environment? In this dissertation, we present a system that allows an agent to learn models of activity and its environment and then use those models to create units of behavior of increasing sophistication for the purpose of achieving its own internally-generated goals.

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