RPLLEARN Extending an Autonomous Robot Control Language to Perform

In this paper, we extend the autonomous robot control and plan language RPL with constructs for specifying experiences, control tasks, learning systems and their parameterization, and exploration strategies. Using these constructs, the learning problems can be represented explicitly and transparentl...

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Veröffentlicht in:Autonomous Agents and Multiagent Systems: Proceedings, 3rd International Joint Conference, New York City, New York, 2004. S. 1022 - 1029
Hauptverfasser: Beetz, Michael, Kirsch, Alexandra, Muller, Armin
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: Washington, DC, USA IEEE Computer Society 19.07.2004
Schriftenreihe:ACM Conferences
Schlagworte:
Computer systems organization > Embedded and cyber-physical systems > Robotics > Robotic components
Computing methodologies > Artificial intelligence > Control methods
Computing methodologies > Artificial intelligence > Control methods > Computational control theory
Software and its engineering > Software notations and tools > Context specific languages > Specialized application languages
ISBN:9781581138641, 1581138644
Online-Zugang:Volltext
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Zusammenfassung:In this paper, we extend the autonomous robot control and plan language RPL with constructs for specifying experiences, control tasks, learning systems and their parameterization, 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 AGILO autonomous robot soccer controllers and show the feasibility and advantages of our approach.
Bibliographie:SourceType-Conference Papers & Proceedings-1
ObjectType-Conference Paper-1
content type line 25
ISBN:9781581138641
1581138644
DOI:10.5555/1018411.1018870