Plan Representation for Robotic Agents

Michael Beetz

Most robotic agents cannot fully exploit plans as resources for better problem-solving performance because of imminent limitations of their plan representations. In this paper we propose plan representations that are, for a given job, representationally and inferentially adequate and inferentially and acquisitionally efficient. We state what these properties mean in the context of robotic agents and describe how plan representations can be designed to satisfy them. The proposed plan representations have been successfully employed in several longterm experiments on autonomous robots.


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