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Knowledge Processing for Cognitive Robots

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Abstract

Knowledge processing methods are an important resource for robots that perform challenging tasks in complex, dynamic environments. When applied to robot control, such methods allow to write more general and flexible control programs and enable reasoning about the robot’s observations, the actions involved in a task, action parameters and the reasons why an action was performed. However, the application of knowledge representation and reasoning techniques to autonomous robots creates several hard research challenges. In this article, we discuss some of these challenges and our approaches to solving them.

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Acknowledgements

This work is supported in part within the DFG excellence initiative research cluster Cognition for Technical Systems (CoTeSys), see also www.cotesys.org.

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Authors and Affiliations

  1. Intelligent Autonomous Systems Group, Technische Universität München, Boltzmannstr. 3, 85748, Garching, Germany

    Moritz Tenorth, Dominik Jain & Michael Beetz

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  1. Moritz Tenorth
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  2. Dominik Jain
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  3. Michael Beetz
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Correspondence to Michael Beetz.

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Tenorth, M., Jain, D. & Beetz, M. Knowledge Processing for Cognitive Robots. Künstl Intell 24, 233–240 (2010). https://doi.org/10.1007/s13218-010-0044-0

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  • DOI: https://doi.org/10.1007/s13218-010-0044-0

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