A MULTI-ISSUE TRUST MODEL IN MULTIAGENT SYSTEMS: A MATHEMATICAL APPROACH

Nguyen Manh Hung, Tran Dinh Que

Abstract


In the recent years, trust has become a crucial issue in studying agentbased
distributed autonomous systems such as Semantic Web or Peerto-
Peer, in which software agents need to select the most trustworthy
partners to interact. Most current computational trust models are mainly
based on two basic factors: personal experience trust and reference trust
on a single issue of trust. These models may be not very fruitful when
applying to trust systems with multi-issue, in which agents need to infer
a trust of some new issue from trusted issues. This status occurs due to
lack of information or uncertainty on both experience trust and reference
trust of the issue. In this paper, we introduce a trust model that is an
extension of the single issue trust one to a multi-issue trust one. Our
approach is to investigate a new type of trust - inference trust, and then
to integrate it into this extension model. The new trust may enable
agents to discover his local knowledge about their partners to infer the
new trust of their partners on some issue.

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