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Reputation assessment : a review and unifying abstraction
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Taylor, Phillip M., Barakat, Lina, Miles, Simon and Griffiths, Nathan (2018) Reputation assessment : a review and unifying abstraction. Knowledge Engineering Review, 33 . e6. doi:10.1017/S0269888918000097 ISSN 0269-8889.
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Official URL: https://doi.org/10.1017/S0269888918000097
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.
Item Type: | Journal Article | ||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
Library of Congress Subject Headings (LCSH): | Multiagent systems, Machine learning | ||||||
Journal or Publication Title: | Knowledge Engineering Review | ||||||
Publisher: | Cambridge University Press | ||||||
ISSN: | 0269-8889 | ||||||
Official Date: | 12 June 2018 | ||||||
Dates: |
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Volume: | 33 | ||||||
Article Number: | e6 | ||||||
DOI: | 10.1017/S0269888918000097 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Copyright Holders: | © Cambridge University Press, 2018 | ||||||
Date of first compliant deposit: | 7 March 2018 | ||||||
Date of first compliant Open Access: | 12 December 2018 | ||||||
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