Fuzzy trust evaluation and credibility development in multi-agent systems

  • Authors:

  • Stefan Schmidt;Robert Steele;Tharam S. Dillon;Elizabeth Chang

  • Affiliations:

  • University of Technology, Sydney, P.O. Box 123 Broadway, NSW 2007, Australia;University of Technology, Sydney, P.O. Box 123 Broadway, NSW 2007, Australia;University of Technology, Sydney, P.O. Box 123 Broadway, NSW 2007, Australia;Curtin University of Technology, G.P.O. Box U1987, PERTH WA 6845, Australia

  • Venue:
  • Applied Soft Computing
  • Year:
  • 2007

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Abstract

E-commerce markets can increase their efficiency through the usage of intelligent agents which negotiate and execute contracts on behalf of their owners. The measurement and computation of trust to secure interactions between autonomous agents is crucial for the success of automated e-commerce markets. Building a knowledge sharing network among peer agents helps to overcome trust-related boundaries in an environment where least human intervention is desired. Nevertheless, a risk management model which allows individual customisation to meet the different security needs of agent-owners is vital. The calculation and measurement of trust in unsupervised virtual communities like multi-agent environments involves complex aspects such as credibility rating for opinions delivered by peer agents, or the assessment of past experiences with the peer node one wishes to interact with. The deployment of suitable algorithms and models imitating human reasoning can help to solve these problems. This paper proposes not only a customisable trust evaluation model based on fuzzy logic but also demonstrates the integration of post-interaction processes like business interaction reviews and credibility adjustment. Fuzzy logic provides a natural framework to deal with uncertainty and the tolerance of imprecise data inputs to fuzzy-based systems makes fuzzy reasoning especially attractive for the subjective tasks of trust evaluation, business-interaction review and credibility adjustment.