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.
|Number of pages||15|
|Journal||Communications in Computer and Information Science|
|Publication status||Published - 2018|
- multi-agent systems
- reinforcement learning