Combining Direct Trust and Indirect Trust in Multi-Agent Systems

Abstract

<jats:p>To assess the trustworthiness of an agent in a multi-agent system, one often combines two types of trust information: direct trust information derived from one's own interactions with that agent, and indirect trust information based on advice from other agents. This paper provides the first systematic study on when it is beneficial to combine these two types of trust as opposed to relying on only one of them. Our large-scale experimental study shows that strong methods for computing indirect trust make direct trust redundant in a surprisingly wide variety of scenarios. Further, a new method for the combination of the two trust types is proposed that, in the remaining scenarios, outperforms the ones known from the literature.</jats:p>

An Adaptive Dempster-Shafer Theory of Evidence Based Trust Model in Multiagent Systems

“…The probability expectation is used to express the trust value. The literature [22] provides a systematic study on when it is beneficial to combine direct trust and indirect reputation. TRAVOS [9] models trustworthiness probabilistically through a beta distribution computed from the outcomes of all the interactions a truster has observed, and it uses the probability of successful interaction between two agents to capture the value of trustworthiness.…”