A Trust-based Mechanism for Avoiding Liars in Referring of Reputation in Multiagent System

Trust is considered as the crucial factor for agents in decision making to choose the most trustworthy partner during their interaction in open distributed multiagent systems. Most current trust models are the combination of experience trust and reference trust, in which the reference trust is estimated from the judgements of agents in the community about a given partner. These models are based on the assumption that all agents are reliable when they share their judgements about a given partner to the others. However, these models are no more longer appropriate to applications of multiagent systems, where several concurrent agents may not be ready to share their private judgement about others or may share the wrong data by lying to their partners. In this paper, we introduce a combination model of experience trust and experience trust with a mechanism to enable agents take into account the trustworthiness of referees when they refer their judgement about a given partner. We conduct experiments to evaluate the proposed model in the context of the e-commerce environment. Our research results suggest that it is better to take into account the trustworthiness of referees when they share their judgement about partners. The experimental results also indicate that although there are liars in the multiagent systems, combination trust computation is better than the trust computation based only on the experience trust of agents.

@article{Nguyen2015,
  title     = {A Trust-based Mechanism for Avoiding Liars in Referring of Reputation in Multiagent System},
  journal   = {International Journal of Advanced Research in Artificial Intelligence},
  volume    = {4},
  number    = {2},
  year      = {2015},
  publisher = {The Science and Information Organization},
  author    = {Manh Hung Nguyen and Dinh Que Tran},
  doi       = {10.14569/IJARAI.2015.040205},
  url       = {https://doi.org/10.14569/IJARAI.2015.040205}
}