Building a fuzzy trust network in unsupervised multi-agent environments

Chang, Elizabeth; Schmidt, S.; Steele, R.; Dillon, Tharam S.

doi:10.1007/11575863_103

Access Status

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Authors

Chang, Elizabeth

Schmidt, S.

Steele, R.

Dillon, Tharam S.

Date

2005

Type

Conference Paper

Metadata

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Citation

Chang, Elizabeth and Schmidt, Stefan and Steele, Robert and Dillon, Tharam. 2005. : Building a fuzzy trust network in unsupervised multi-agent environments, in Herrero, P. and Meersman, R. and Tari, Z. (ed), On the Move to Meaningful Internet Systems 2005: OTM 2005 Workshop, Oct 31 2005, pp. 816-825. Agia Napa, Cyprus: Springer-Verlag.

Source Title

Lecture Notes in Computer Science: On the Move to Meaningful Internet Systems 2005: OTM Workshops: OTM Confederated International Workshops and Posters, AWeSOMe, CAMS, GADA, MIOS+INTEROP, ORM, PhDS, SeBGIS, SWWS, and WOSE 2005

Source Conference

On the Move to Meaningful Internet Systems 2005: OTM 2005 Workshop

DOI

10.1007/11575863_103

Faculty

Curtin Business School

School of Information Systems

School

Centre for Extended Enterprises and Business Intelligence

URI

http://hdl.handle.net/20.500.11937/22908

Collection

Curtin Research Publications

Abstract

In automated and unsupervised multi-agent environments, where agents act on behalf of their stakeholders, the measurement and computation of trust is a key building block upon which all business interaction scenarios rely. In environments, where the individual and independent calculation of trustworthiness values for future negotiation partners is desired, flexible algorithms and models imitating human reasoning are crucial. This paper introduces a trust evaluation model that imitates human reasoning by using fuzzy logic concepts. Furthermore, post-interaction processes such as business interaction reviews and credibility adjustment are used to continuously build and refine an information repository for future trust evaluation processes. Fuzzy logic offers a mathematical approach encompassing uncertainty and tolerance of imprecise data, and combined with our highly customizable model, it allows to meet the security needs of different stakeholders.