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 |
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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