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Reputation assessment: a review and unifying abstraction

Published online by Cambridge University Press:  12 June 2018

Phillip Taylor ,
Lina Barakat ,
Simon Miles  and
Nathan Griffiths
Phillip Taylor
Affiliation:
Department of Computer Science, University of Warwick, CoventryCV4 7AL, UK e-mail: phillip.taylor@warwick.ac.uk, nathan.griffiths@warwick.ac.uk
Lina Barakat
Affiliation:
Department of Informatics, King’s College London, Strand, London WC2R 2LS, UK e-mail: lina.barakat@kcl.ac.uk, simon.miles@kcl.ac.uk
Simon Miles
Affiliation:
Department of Informatics, King’s College London, Strand, London WC2R 2LS, UK e-mail: lina.barakat@kcl.ac.uk, simon.miles@kcl.ac.uk
Nathan Griffiths
Affiliation:
Department of Computer Science, University of Warwick, CoventryCV4 7AL, UK e-mail: phillip.taylor@warwick.ac.uk, nathan.griffiths@warwick.ac.uk
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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.

Type
Survey Article
Information
The Knowledge Engineering Review , Volume 33 , 2018 , e6
Copyright
© Cambridge University Press, 2018 

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