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Often in open multiagent systems, agents interact with other agents to meet their own goals. Trust is, therefore, considered essential to make such interactions effective. However, trust is a complex, multifaceted concept and includes more than just evaluating others’ honesty. Many trust evaluation models have been proposed and implemented in different areas; most of them focused on algorithms for trusters to model the trustworthiness of trustees in order to make effective decisions about which trustees to select. For this purpose, many trust evaluation models use third party information sources such as witnesses, but slight consideration is paid for locating such third party information sources. Unlike most trust models, the proposed model defines a scalable way to locate a set of witnesses, and combines a suspension technique with reinforcement learning to improve the model responses to dynamic changes in the system. Simulation results indicate that the proposed model benefits trusters while demanding less message overhead.

A decentralized trustworthiness estimation model for open, multiagent systems (DTMAS)