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.
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.
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 language | English |
|---|---|
| Title of host publication | Artificial Intelligence |
| Subtitle of host publication | 29th Benelux Conference, BNAIC 2017, Groningen, The Netherlands, November 8–9, 2017, Revised Selected Papers |
| Editors | Bart Verheij, Marco Wiering |
| Publisher | Springer, Cham |
| Pages | 1-15 |
| Number of pages | 15 |
| ISBN (Electronic) | 978-3-319-76892-2 |
| ISBN (Print) | 978-3-319-76891-5 |
| DOIs | |
| Publication status | Published - 2018 |
Publication series
| Series | Communications in Computer and Information Science |
|---|---|
| Volume | 823 |
| ISSN | 1865-0929 |
Keywords
- diagnosis
- multi-agent systems
- reinforcement learning