Routine interactions in the world of an autonomous agent are a major source of learning for the agent. In my approach an agent interacts in the world in several different ways, from cognitive to automatic. I show that an agent can learn and also improve its routine interactions in its different modes of interaction in the world.
I present a formalism and use for a goal structure known as goal sketch . Rewards and punishments generated from a goal sketch which indicate progress in goal satisfaction are used to improve automatic interactions and enhance agent's strategies and concepts about action. I will discuss my experiments with a physical robot that uses a goal sketch in order to generate rewards and punishments which are then used in improving robot skills and discovering new actions.
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