Ranking-Based Partner Selection Strategy in Open, Dynamic and Sociable Environments
Publication Name
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Abstract
Agents with limited capacities need to cooperate with others to fulfil complex tasks in a multi-agent system. To find a reliable partner, agents with insufficient experience have to seek advice from advisors. Currently, most models are rating-based, aggregating advisors’ information on partners and calculating averaged results. These models have some drawbacks, like being vulnerable to unfair ratings under a high ratio of dishonest advisors or dynamic attacks and locally convergent. Therefore, this paper proposes a Ranking-based Partner Selection (RPS) model, which clusters honest and dishonest advisors into different groups based on their different rankings of trustees. Besides, RPS uses a sliding-window-based method to find dishonest advisors with dynamic attack behaviours. Furthermore, RPS utilizes an online-learning method to update model parameters based on real-time interaction results. According to experiment results, RPS outperforms ITEA under different kinds of unfair rating attacks, especially in two situations: 1) there is a high ratio of dishonest advisors; 2)dishonest advisor takes dynamic attack strategies.
Open Access Status
This publication is not available as open access
Volume
14546 LNAI
First Page
267
Last Page
285