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 publicationBenelux Artificial Intelligence Conference (BNAIC)
Number of pages15
Publication statusPublished - 2017

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