Learning-Based Diagnosis and Repair

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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 learningbased 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.
Original languageEnglish
Title of host publicationArtificial Intelligence
Subtitle of host publication29th Benelux Conference, BNAIC 2017, Groningen, The Netherlands, November 8–9, 2017, Revised Selected Papers
EditorsBart Verheij, Marco Wiering
PublisherSpringer, Cham
Number of pages15
ISBN (Electronic)978-3-319-76892-2
ISBN (Print)978-3-319-76891-5
Publication statusPublished - 2018

Publication series

SeriesCommunications in Computer and Information Science


  • diagnosis
  • multi-agent systems
  • reinforcement learning

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