Learning-Based Diagnosis and Repair

Nico Roos

Learning-Based Diagnosis and Repair

Robots, Agents, Interaction

Research output: Contribution to journal › Article › Academic › peer-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 language	English
Pages (from-to)	1-15
Number of pages	15
Journal	Communications in Computer and Information Science
Volume	823
Publication status	Published - 2018

Keywords

diagnosis
multi-agent systems
reinforcement learning

Cite this

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title = "Learning-Based Diagnosis and Repair",

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.",

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author = "Nico Roos",

year = "2018",

language = "English",

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journal = "Communications in Computer and Information Science",

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publisher = "Springer International Publishing",

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KW - multi-agent systems

KW - reinforcement learning

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JO - Communications in Computer and Information Science

JF - Communications in Computer and Information Science

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