Using Fuzzy Logic and Q-Learning for Trust Modeling in Multi-agent Systems
Abdullah Aref, Thomas Tran
Citation: Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 2, pages 59–66 (2014)
Abstract. Often in multi-agent systems, agents interact with other agents to fulfill their own goals. Trust is, therefore, considered essential to make such interactions effective. This work describes a trust model that augments fuzzy logic with Q-learning to help trust evaluating agents select beneficial trustees for interaction in uncertain, open, dynamic, and untrusted multi-agent systems. The performance of the proposed model is evaluated using simulation. The simulation results indicate that the proper augmentation of fuzzy subsystem to Q-learning can be useful for trust evaluating agents, and the resulting model can respond to dynamic changes in the environment.