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13 May 2019 Collective trust estimation in multi-agent systems
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Abstract
In previous work, a multi-layered neural network trust model, dubbed NeuroTrust, was introduced. This trust model was also implemented in an autonomous vehicles convoy simulation, in which speed and gap distance depended on trust. It has been shown that, in time, through on-line reinforcement learning, this trust model produces better results for significant performance metrics in the respective autonomous vehicle convoy when compared to a baseline trust algorithm. In this paper, the NeuroTrust model is expanded to leverage the experience of multiple decision-making agents. A trust aggregation method is proposed for NeuroTrust and is simulated for multiple autonomous vehicle convoy scenarios. It is shown that the NeuroTrust model tends to optimize faster by leveraging each agent’s experience.
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Cristian Balas, Robert Karlsen, Paul Muench, Dariusz Mikulski, Utayba Mohammad, and Nizar Al-Holou "Collective trust estimation in multi-agent systems", Proc. SPIE 11021, Unmanned Systems Technology XXI, 110210O (13 May 2019); https://doi.org/10.1117/12.2518751
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