Learning-Based Diagnosis and Repair

Research output: Contribution to journalArticleAcademicpeer-review

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.
Original languageEnglish
Pages (from-to)1-15
Number of pages15
JournalCommunications in Computer and Information Science
Volume823
Publication statusPublished - 2018

Keywords

  • diagnosis
  • multi-agent systems
  • reinforcement learning

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