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RPL LEARN : Extending an Autonomous Robot Control Language to Perform Experience-based Learning

Abstract : In this paper, we extend the autonomous robot control and plan language RPL with constructs for specifying experiences , control tasks, learning systems and their param-eterization, and exploration strategies. Using these constructs , the learning problems can be represented explicitly and transparently and become executable. With the extended language we rationally reconstruct parts of the AG-ILO autonomous robot soccer controllers and show the feasibility and advantages of our approach.
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https://hal.archives-ouvertes.fr/hal-01306985
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Michael Beetz, Alexandra Kirsch, Armin Müller. RPL LEARN : Extending an Autonomous Robot Control Language to Perform Experience-based Learning. 3rd International Joint Conference on Autonomous Agents & Multi Agent Systems (AAMAS-2004), Jul 2004, New York, United States. pp.1022-1029, ⟨10.1109/AAMAS.2004.2⟩. ⟨hal-01306985⟩

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