Hostname: page-component-586b7cd67f-vdxz6 Total loading time: 0 Render date: 2024-11-25T02:46:16.874Z Has data issue: false hasContentIssue false
  • English
  • Français

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: [email protected], [email protected]
Lina Barakat
Affiliation:
Department of Informatics, King’s College London, Strand, London WC2R 2LS, UK e-mail: [email protected], [email protected]
Simon Miles
Affiliation:
Department of Informatics, King’s College London, Strand, London WC2R 2LS, UK e-mail: [email protected], [email protected]
Nathan Griffiths
Affiliation:
Department of Computer Science, University of Warwick, CoventryCV4 7AL, UK e-mail: [email protected], [email protected]
Get access Rights & Permissions [Opens in a new window]

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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aref, A. M. & Tran, T. T. 2015. A decentralized trustworthiness estimation model for open, multiagent systems (DTMAS). Journal of Trust Management 2(1), 1.Google Scholar
Artz, D. & Gil, Y. 2007. A survey of trust in computer science and the semantic web. Web Semantics: Science, Services and Agents on the World Wide Web 5(2), 58–71.Google Scholar
Ashri, R., Ramchurn, S. D., Sabater, J., Luck, M. & Jennings, N. R. 2005. Trust evaluation through relationship analysis. In Proceedings of the 4th International Joint Conference on Autonomous Agents and Multiagent Systems, 1005–1011. ACM.Google Scholar
Breiman, L. 1996. Bagging predictors. Machine Learning 24(2), 123–140.Google Scholar
Breiman, L. 2001. Random forests. Machine Learning 45(1), 5–32.Google Scholar
Burnett, C., Norman, T. J. & Sycara, K. 2010. Bootstrapping trust evaluations through stereotypes. In Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems, 241–248.Google Scholar
Burnett, C., Norman, T. & Sycara, K. 2013. Stereotypical trust and bias in dynamic multiagent systems. ACM Transactions on Intelligent Systems and Technology 4(2), 26.Google Scholar
Fang, H., Zhang, J., Şensoy, M. & Thalmann, N. 2012. A generalized stereotypical trust model. In 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications, 698–705.Google Scholar
Griffiths, N. 2006. Enhancing peer-to-peer collaboration using trust. Expert Systems with Applications 31(4), 849–858.Google Scholar
Hendrikx, F., Bubendorfer, K. & Chard, R. 2015. Reputation systems: a survey and taxonomy. Journal of Parallel and Distributed Computing 75, 184–197.Google Scholar
Ho, T. K. 1998. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 832–844.Google Scholar
Hoelz, B. W. P. & Ralha, C. G. 2015. Towards a cognitive meta-model for adaptive trust and reputation in open multi-agent systems. Autonomous Agents and Multi-Agent Systems 29(6), 1125–1156.Google Scholar
Huynh, T. D., Jennings, N. R. & Shadbolt, N. R 2004. FIRE: An integrated trust and reputation model for open multi-agent systems. In 16th European Conference on Artificial Intelligence, 18–22.Google Scholar
Huynh, T. D., Jennings, N. R. & Shadbolt, N. R. 2006. An integrated trust and reputation model for open multi-agent systems. Autonomous Agents and Multi-Agent Systems 13(2), 119–154.Google Scholar
Jøsang, A. & Ismail, R. 2002. The Beta reputation system. In Proceedings of the 15th Bled Electronic Commerce Conference, 41–55.Google Scholar
Jøsang, A., Ismail, R. & Boyd, C. 2007. A survey of trust and reputation systems for online service provision. Decision Support Systems 43(2), 618–644.Google Scholar
Kinateder, M., Baschny, E. & Rothermel, K. 2005. Towards a generic trust model–comparison of various trust update algorithms. In International Conference on Trust Management, 177–192. Springer.Google Scholar
Koster, A., Sabater-Mir, J. & Schorlemmer, M. 2012. Personalizing communication about trust. In Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems, 517–524.Google Scholar
Koster, A., Schorlemmer, M. & Sabater-Mir, J. 2012. Engineering trust alignment: theory, method and experimentation. International Journal of Human-Computer Studies 70, 450–473.Google Scholar
Liu, X. & Datta, A. 2012. Modeling context aware dynamic trust using hidden Markov model. In Proceedings of the 26th AAAI Conference on Artificial Intelligence , AAAI’12, 1938–1944.Google Scholar
Liu, X., Datta, A. & Rzadca, K. 2013. Trust beyond reputation: a computational trust model based on stereotypes. Electronic Commerce Research and Applications 12, 24–39.Google Scholar
Liu, X., Datta, A., Rzadca, K. & Lim, E.-P. 2009. StereoTrust: a group based personalized trust model. In Proceedings of the 18th ACM Conference on Information and Knowledge Management, 7–16. ACM.Google Scholar
Liu, X., Kaszuba, T., Nielek, R., Datta, A. & Wierzbicki, A. 2010. Using stereotypes to identify risky transactions in internet auctions. In 2010 IEEE Second International Conference on Social Computing, 513–520.Google Scholar
Liu, X., Tredan, G. & Datta, A. 2014. A generic trust framework for large-scale open systems using machine learning. Computational Intelligence 30(4), 700–721.Google Scholar
Marsh, S. 1994. Trust in Distributed Artificial Intelligence. Springer, pages, 94–112.Google Scholar
Miles, S. & Griffiths, N. 2015a. Incorporating mitigating circumstances into reputation assessment. In Proceedings of the 2nd International Workshop on Multiagent Foundations of Social Computing.Google Scholar
Miles, S. & Griffiths, N. 2015b. Accounting for circumstances in reputation assessment. In Proceedings of the 14th International Conference on Autonomous Agents and Multiagent Systems, 1653–1654.Google Scholar
Muller, T., Liu, Y. & Zhang, J. 2015. The fallacy of endogenous discounting of trust recommendations. In Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, 563–572.Google Scholar
Quinlan, R. J. 1992. Learning with continuous classes. In 5th Australian Joint Conference on Artificial Intelligence, 343–348. World Scientific.Google Scholar
Regan, K., Poupart, P. & Cohen, R. 2006. Bayesian reputation modeling in e-marketplaces sensitive to subjectivity, deception and change. In Proceedings of the National Conference on Artificial Intelligence, 1206–1212.Google Scholar
Sabater, J. & Sierra, C. 2001a. Regret: a reputation model for gregarious societies. In 4th Workshop on Deception Fraud and Trust in Agent Societies, 61–69.Google Scholar
Sabater, J. & Sierra, C. 2001b. Social regret, a reputation model based on social relations. ACM SIGecom Exchanges 3(1), 44–56.Google Scholar
Schillo, M., Funk, P. & Rovatsos, M. 2000. Using trust for detecting deceitful agents in artificial societies. Applied Artificial Intelligence 14(8), 825–848.Google Scholar
Şensoy, M., Zhang, J., Yolum, P. & Cohen, R. 2009. Poyraz: context-aware service selection under deception. Computational Intelligence 25(4), 335–366.Google Scholar
Şensoy, M., Yilmaz, B. & Norman, T. J. 2016. STAGE: stereotypical trust assessment through graph extraction. Computational Intelligence 32(1), 72–101.Google Scholar
Taylor, P., Griffiths, N., Barakat, L. & Miles, S. 2017a. Stereotype reputation with limited observability. In 19th International Workshop on Trust in Agent Societies.Google Scholar
Taylor, P., Griffiths, N., Barakat, L. & Miles, S. 2017b. Bootstrapping trust with partial and subjective observability. In Proceedings of the 16th Conference on Autonomous Agents and Multiagent Systems.Google Scholar
Teacy, L., Patel, J., Jennings, N. & Luck, M. 2005. Coping with inaccurate reputation sources: experimental analysis of a probabilistic trust model. In Proceedings of the 4th International Joint Conference on Autonomous Agents and Multiagent Systems, 997–1004.Google Scholar
Teacy, L., Luck, M., Rogers, A. & Jennings, N. 2012. An efficient and versatile approach to trust and reputation using hierarchical Bayesian modelling. Artificial Intelligence 193, 149–185.Google Scholar
Urbano, J., Rocha, A. P. & Oliveira, E. C. 2010. Refining the trustworthiness assessment of suppliers through extraction of stereotypes. In Proceedings of the International Conference on Enterprise Information Systems, 85–92.Google Scholar
Vogiatzis, G., MacGillivray, I. & Chli, M. 2010. A probabilistic model for trust and reputation. In Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1, 1, 225–232. International Foundation for Autonomous Agents and Multiagent Systems.Google Scholar
Whitby, A., Jøsang, A. & Indulska, J. 2004. Filtering out unfair ratings in Bayesian reputation systems. In Proceedings of the 7th International Workshop on Trust in Agent Societies, 6, 106–117.Google Scholar
Witten, I., Frank, E. & Hall, M. 2011. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.Google Scholar
Wooldridge, M. 2009. An Introduction to Multiagent Systems. John Wiley & Sons.Google Scholar
Yu, H., Shen, Z., Leung, C., Miao, C. & Lesser, V. 2013. A survey of multi-agent trust management systems. IEEE Access 1, 35–50.Google Scholar
Zacharia, G. & Maes, P. 2000. Trust management through reputation mechanisms. Applied Artificial Intelligence 14(9), 881–907.Google Scholar