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2018 Volume 33
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RESEARCH ARTICLE   Open Access    

Reputation assessment: a review and unifying abstraction

  • 1.

    Department of Computer Science

  • 2.

    Department of Informatics

More Information
  • Abstract: Trust and reputation allow agents to make informed decisions about potential interactions. Trust in an agent is derived from direct experience with that agent, while reputation is determined by the experiences reported by other witness agents with potentially differing viewpoints. These experiences are typically aggregated in a trust and reputation model, of which there are several types that focus on different aspects. Such aspects include handling subjective perspectives of witnesses, dishonesty, or assessing the reputation of new agents. In this paper, we distil reputation systems into their fundamental aspects, discussing first how trust and reputation information is represented and second how it is disseminated among agents. Based on these discussions, a unifying abstraction is presented for trust and reputation systems, which is demonstrated by instantiating it with a broad range of reputation systems found in the literature. The abstraction is then instantiated to combine the range of capabilities of existing reputation systems in the Machine Learning Reputation System, which is evaluated using a marketplace simulation.
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  • Cite this article

    Phillip Taylor, Lina Barakat, Simon Miles, Nathan Griffiths. 2018. Reputation assessment: a review and unifying abstraction. The Knowledge Engineering Review 33(1), doi: 10.1017/S0269888918000097
    Phillip Taylor, Lina Barakat, Simon Miles, Nathan Griffiths. 2018. Reputation assessment: a review and unifying abstraction. The Knowledge Engineering Review 33(1), doi: 10.1017/S0269888918000097
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RESEARCH ARTICLE   Open Access    

Reputation assessment: a review and unifying abstraction

  • 1.

    Department of Computer Science

  • 2.

    Department of Informatics

  • Published online: 12 June 2018
The Knowledge Engineering Review  33,  2018, 33(1)  |  Cite this article

Abstract: Abstract: Trust and reputation allow agents to make informed decisions about potential interactions. Trust in an agent is derived from direct experience with that agent, while reputation is determined by the experiences reported by other witness agents with potentially differing viewpoints. These experiences are typically aggregated in a trust and reputation model, of which there are several types that focus on different aspects. Such aspects include handling subjective perspectives of witnesses, dishonesty, or assessing the reputation of new agents. In this paper, we distil reputation systems into their fundamental aspects, discussing first how trust and reputation information is represented and second how it is disseminated among agents. Based on these discussions, a unifying abstraction is presented for trust and reputation systems, which is demonstrated by instantiating it with a broad range of reputation systems found in the literature. The abstraction is then instantiated to combine the range of capabilities of existing reputation systems in the Machine Learning Reputation System, which is evaluated using a marketplace simulation.

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    Acknowledgments

    • This work was part funded by the UK Engineering and Physical Sciences Research Council as part of the Justified Assessments of Service Provider Reputation project, ref. EP/M012654/1 and EP/M012662/1.

    • github.com/jaspr-project/MLReputationTestBed

    • github.com/jaspr-project/MLReputationTestBed

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    Cite this article
    Phillip Taylor, Lina Barakat, Simon Miles, Nathan Griffiths. 2018. Reputation assessment: a review and unifying abstraction. The Knowledge Engineering Review 33(1), doi: 10.1017/S0269888918000097
    Phillip Taylor, Lina Barakat, Simon Miles, Nathan Griffiths. 2018. Reputation assessment: a review and unifying abstraction. The Knowledge Engineering Review 33(1), doi: 10.1017/S0269888918000097
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