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Annals of Computer Science and Information Systems, Volume 2

Proceedings of the 2014 Federated Conference on Computer Science and Information Systems

Using Fuzzy Logic and Q-Learning for Trust Modeling in Multi-agent Systems

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DOI: http://dx.doi.org/10.15439/2014F482

Citation: Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 2, pages 5966 ()

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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.