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Certified Trust Model

  • Vanderson Botêlho
  • Fabríco Enembreck
  • Bráulio C. ávila
  • Hilton de Azevedo
  • Edson E. Scalabrin
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 296)

Abstract

This paper presents a certified confidence model which aims to ensure credibility for information exchanged among agents which inhabit an open environment. Generally speaking, the proposed environment shows a supplier agent b which delivers service for a customer agent a. The agent a returns to b a crypto-graphed evaluation r on the service delivered. The agent b will employ R as testimonial when requested to perform the same task for a distinct customer agent. Our hypotheses are: (i) control over testimonials can be distributed as they are locally stored by the assessed agents, i.e., each assessed agent is the owner of its testimonials; and (ii) testimonials, provided by supplier agents on their services, can be considered reliable since they are encapsulated with public key cryptography. This approach reduces the limitations of confidence models based, respectively, on the experience resulted from direct interaction between agents (direct confidence) and on the indirect experience obtained from reports of witnesses (propagated confidence). Direct confidence is a poor-quality measure for a customer agent a hardly has enough opportunities to interact with a supplier agent b so as to grow a useful knowledge base. Propagated confidence depends on the willingness of witnesses to share their experiences. The empiric model was tested in a multiagent system applied to the stock market, where supplier agents provide recommendations for buying or selling assets and customer agents then choose suppliers based on their reputations. Results demonstrate that the confidence model proposed enables the agents to more efficiently choose partners.

Keywords

Trust Model Multiagent System Autonomous Agent Direct Trust Good Provider 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© IFIP International Federation for Information Processing 2009

Authors and Affiliations

  • Vanderson Botêlho
    • 1
  • Fabríco Enembreck
    • 1
  • Bráulio C. ávila
    • 1
  • Hilton de Azevedo
    • 2
  • Edson E. Scalabrin
    • 1
  1. 1.PUCPR, Pontifical Catholic University of Paraná PPGIA, Graduate Program on Applied Computer ScienceCuritibaBrazil
  2. 2.UTFPR, Federal Technological University of Paraná PPGTE, Graduate Program on TechnologyCuritibaBrazil

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