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Learning-Based Diagnosis and Repair

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Artificial Intelligence (BNAIC 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 823))

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Abstract

This paper proposes a new form of diagnosis and repair based on reinforcement learning. Self-interested agents learn locally which agents may provide a low quality of service for a task. The correctness of learned assessments of other agents is proved under conditions on exploration versus exploitation of the learned assessments.

Compared to collaborative multi-agent diagnosis, the proposed learning-based approach is not very efficient. However, it does not depend on collaboration with other agents. The proposed learning based diagnosis approach may therefore provide an incentive to collaborate in the execution of tasks, and in diagnosis if tasks are executed in a suboptimal way.

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Notes

  1. 1.

    In smart energy networks the tasks are the directions in which energy must flow.

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Author information

Authors and Affiliations

  1. Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands

    Nico Roos

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  1. Nico Roos
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Correspondence to Nico Roos .

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Editors and Affiliations

  1. Artificial Intelligence, University of Groningen, Groningen, The Netherlands

    Bart Verheij

  2. Artificial Intelligence, University of Groningen, Groningen, The Netherlands

    Marco Wiering

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Roos, N. (2018). Learning-Based Diagnosis and Repair. In: Verheij, B., Wiering, M. (eds) Artificial Intelligence. BNAIC 2017. Communications in Computer and Information Science, vol 823. Springer, Cham. https://doi.org/10.1007/978-3-319-76892-2_1

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  • DOI: https://doi.org/10.1007/978-3-319-76892-2_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76891-5

  • Online ISBN: 978-3-319-76892-2

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