Detecting deception in reputation management
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
TRAVOS: Trust and Reputation in the Context of Inaccurate Information Sources
Autonomous Agents and Multi-Agent Systems
Multi-robot learning with particle swarm optimization
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Agent-Based Evolutionary Search
Agent-Based Evolutionary Search
iCLUB: an integrated clustering-based approach to improve the robustness of reputation systems
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
A multiagent evolutionary framework based on trust for multiobjective optimization
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Towards the design of robust trust and reputation systems
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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In reputation systems for multiagent-based e-marketplaces, buying agents model the reputation of selling agents based on ratings shared by other buyers (called advisors). With the existence of unfair rating attacks from dishonest advisors, the effectiveness of reputation systems thus heavily relies on whether buyers can accurately determine which advisors to include in trust networks and their trustworthiness. In this paper, we propose a novel multiagent evolutionary trust model (MET) where each buyer evolves its trust network. In each generation, each buyer acquires trust network information from its advisors and generates a candidate trust network using evolutionary operators. Only trust networks providing more accurate seller reputation estimation shall survive to the next generation. Experimental results demonstrate MET is more robust than the state-of-the-art trust models against various unfair rating attacks.