The Library
Improving trust and reputation assessment with dynamic behaviour
Tools
Player, Caroline and Griffiths, Nathan (2020) Improving trust and reputation assessment with dynamic behaviour. The Knowledge Engineering Review, 35 . e29. doi:10.1017/S0269888920000077 ISSN 0269-8889.
|
PDF
WRAP-improving-trust-reputation-dynamic-behaviour-Player-2019.pdf - Accepted Version - Requires a PDF viewer. Download (2535Kb) | Preview |
Official URL: https://doi.org/10.1017/S0269888920000077
Abstract
Trust between agents in multi-agent systems (MASs) is critical to encourage high levels of cooperation. Existing methods to assess trust and reputation use direct and indirect past experiences about an agent to estimate their future performance; however, these will not always be representative if agents change their behaviour over time.
Real-world distributed networks such as online market places, P2P networks, pervasive computing and the Smart Grid can be viewed as MAS. Dynamic agent behaviour in such MAS can arise from seasonal changes, cheaters, supply chain faults, network traffic and many other reasons. However, existing trust and reputation models use limited techniques, such as forgetting factors and sliding windows, to account for dynamic behaviour.
In this paper, we propose Reacting and Predicting in Trust and Reputation (RaPTaR), a method to extend existing trust and reputation models to give agents the ability to monitor the output of interactions with a group of agents over time to identify any likely changes in behaviour and adapt accordingly. Additionally, RaPTaR can provide an a priori estimate of trust when there is little or no interaction data (either because an agent is new or because a detected behaviour change suggests recent past experiences are no longer representative). Our results show that RaPTaR has improved performance compared to existing trust and reputation methods when dynamic behaviour causes the ranking of the best agents to interact with to change.
Item Type: | Journal Article | ||||||
---|---|---|---|---|---|---|---|
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, Software engineering, Computer systems -- Reliability, Distributed artificial intelligence, Trust, Computer Communication Networks | ||||||
Journal or Publication Title: | The Knowledge Engineering Review | ||||||
Publisher: | Cambridge University Press | ||||||
ISSN: | 0269-8889 | ||||||
Official Date: | 17 June 2020 | ||||||
Dates: |
|
||||||
Volume: | 35 | ||||||
Article Number: | e29 | ||||||
DOI: | 10.1017/S0269888920000077 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Date of first compliant deposit: | 3 January 2020 | ||||||
Date of first compliant Open Access: | 17 December 2020 | ||||||
Related URLs: |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year