Combining Direct Trust and Indirect Trust in Multi-Agent Systems
Abstract
To assess the trustworthiness of an agent in a multi-agent system, one often combines two types of trust information: direct trust information derived from one's own interactions with that agent, and indirect trust information based on advice from other agents. This paper provides the first systematic study on when it is beneficial to combine these two types of trust as opposed to relying on only one of them. Our large-scale experimental study shows that strong methods for computing indirect trust make direct trust redundant in a surprisingly wide variety of scenarios. Further, a new method for the combination of the two trust types is proposed that, in the remaining scenarios, outperforms the ones known from the literature.
Cite
Text
Parhizkar et al. "Combining Direct Trust and Indirect Trust in Multi-Agent Systems." International Joint Conference on Artificial Intelligence, 2020. doi:10.24963/IJCAI.2020/44Markdown
[Parhizkar et al. "Combining Direct Trust and Indirect Trust in Multi-Agent Systems." International Joint Conference on Artificial Intelligence, 2020.](https://mlanthology.org/ijcai/2020/parhizkar2020ijcai-combining/) doi:10.24963/IJCAI.2020/44BibTeX
@inproceedings{parhizkar2020ijcai-combining,
title = {{Combining Direct Trust and Indirect Trust in Multi-Agent Systems}},
author = {Parhizkar, Elham and Nikravan, Mohammad Hossein and Holte, Robert C. and Zilles, Sandra},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2020},
pages = {311-317},
doi = {10.24963/IJCAI.2020/44},
url = {https://mlanthology.org/ijcai/2020/parhizkar2020ijcai-combining/}
}