A biologically inspired computational trust model for open multi-agent systems which is resilient to trustor population changes
<|reference_start|>A biologically inspired computational trust model for open multi-agent systems which is resilient to trustor population changes: Current trust and reputation models continue to have significant limitations, such as the inability to deal with agents constantly entering or exiting open multi-agent systems (open MAS), as well as continuously changing behaviors. Our study is based on CA, a previously proposed decentralized computational trust model from the trustee's point of view, inspired by synaptic plasticity and the formation of assemblies in the human brain. It is designed to meet the requirements of highly dynamic and open MAS, and its main difference with most conventional trust and reputation models is that the trustor does not select a trustee to delegate a task; instead, the trustee determines whether it is qualified to successfully execute it. We ran a series of simulations to compare CA model to FIRE, a well-established, decentralized trust and reputation model for open MAS under conditions of continuous trustee and trustor population replacement, as well as continuous change of trustees' abilities to perform tasks. The main finding is that FIRE is superior to changes in the trustee population, whereas CA is resilient to the trustor population changes. When the trustees switch performance profiles FIRE clearly outperforms despite the fact that both models' performances are significantly impacted by this environmental change. Findings lead us to conclude that learning to use the appropriate trust model, according to the dynamic conditions in effect could maximize the trustor's benefits.<|reference_end|>
@article{lygizou2024a,
    title={A biologically inspired computational trust model for open multi-agent
  systems which is resilient to trustor population changes},
    author={Zoi Lygizou and Dimitris Kalles},
    journal={arXiv preprint arXiv:2404.10014},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.10014},
    primaryClass={cs.MA cs.AI}
}