Authors:
Qin Liang
1
;
2
;
Wen Gu
3
;
Shohei Kato
2
;
Fenghui Ren
1
;
Guoxin Su
1
;
Takayuki Ito
4
and
Minjie Zhang
1
Affiliations:
1
University of Wollongong, Wollongong, Australia
;
2
Nagoya Institute of Technology, Nagoya, Japan
;
3
Japan Advanced Institute of Science and Technology, Nomi, Japan
;
4
Kyoto University, Kyoto, Japan
Keyword(s):
Advisor, Partner Selection, Unfair Rating Attacks, Ranking.
Abstract:
In multi-agent systems, agents with limited capabilities need to find a cooperation partner to accomplish complex tasks. Evaluating the trustworthiness of potential partners is vital in partner selection. Current approaches are mainly averaged-based, aggregating advisors’ information on partners. These methods have limitations, such as vulnerability to unfair rating attacks, and may be locally convergent that cannot always select the best partner. Therefore, we propose a ranking-based partner selection (RPS) mechanism, which clusters advisors into groups according to their ranking of trustees and gives recommendations based on groups. Besides, RPS is an online-learning method that can adjust model parameters based on feedback and evaluate the stability of advisors’ ranking behaviours. Experiments demonstrate that RPS performs better than state-of-the-art models in dealing with unfair rating attacks, especially when dishonest advisors are the majority.