Abstract
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 [11]. 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.
This work was supported in part by Equipment Grant No. EDUD-US-932022 from SUN Microsystems Computer Corporation and in part by NASA under contract NAS 9-19004.
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References
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© 1996 Springer-Verlag Berlin Heidelberg
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Hexmoor, H.H. (1996). Learning routines. In: Wooldridge, M., Müller, J.P., Tambe, M. (eds) Intelligent Agents II Agent Theories, Architectures, and Languages. ATAL 1995. Lecture Notes in Computer Science, vol 1037. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3540608052_61
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DOI: https://doi.org/10.1007/3540608052_61
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