Learning Trust Over Directed Graphs in Multiagent Systems (extended version)
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2022
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914898022236160 |
|---|---|
| author | Akgün, Orhan Eren Dayı, Arif Kerem Gil, Stephanie Nedić, Angelia |
| author_facet | Akgün, Orhan Eren Dayı, Arif Kerem Gil, Stephanie Nedić, Angelia |
| contents | We address the problem of learning the legitimacy of other agents in a multiagent network when an unknown subset is comprised of malicious actors. We specifically derive results for the case of directed graphs and where stochastic side information, or observations of trust, is available. We refer to this as ``learning trust'' since agents must identify which neighbors in the network are reliable, and we derive a protocol to achieve this. We also provide analytical results showing that under this protocol i) agents can learn the legitimacy of all other agents almost surely, and that ii) the opinions of the agents converge in mean to the true legitimacy of all other agents in the network. Lastly, we provide numerical studies showing that our convergence results hold in practice for various network topologies and variations in the number of malicious agents in the network. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2212_02661 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Learning Trust Over Directed Graphs in Multiagent Systems (extended version) Akgün, Orhan Eren Dayı, Arif Kerem Gil, Stephanie Nedić, Angelia Multiagent Systems We address the problem of learning the legitimacy of other agents in a multiagent network when an unknown subset is comprised of malicious actors. We specifically derive results for the case of directed graphs and where stochastic side information, or observations of trust, is available. We refer to this as ``learning trust'' since agents must identify which neighbors in the network are reliable, and we derive a protocol to achieve this. We also provide analytical results showing that under this protocol i) agents can learn the legitimacy of all other agents almost surely, and that ii) the opinions of the agents converge in mean to the true legitimacy of all other agents in the network. Lastly, we provide numerical studies showing that our convergence results hold in practice for various network topologies and variations in the number of malicious agents in the network. |
| title | Learning Trust Over Directed Graphs in Multiagent Systems (extended version) |
| topic | Multiagent Systems |
| url | https://arxiv.org/abs/2212.02661 |