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- W2019522929 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." @default.
- W2019522929 created "2016-06-24" @default.
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- W2019522929 date "2014-09-29" @default.
- W2019522929 modified "2023-10-16" @default.
- W2019522929 title "Using Fuzzy Logic and Q-Learning for Trust Modeling in Multi-agent Systems" @default.
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- W2019522929 doi "https://doi.org/10.15439/2014f482" @default.
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