Department of Computer Science, University of Warwick, CoventryCV4 7AL, UK e-mails: c.e.player@warwick.ac.uk, n.e.griffiths@warwick.ac.uk"/>
Search
  • All Journals
Advanced Search
Search articles by Title, Author, Keywords, DOI
Submit Manuscript
7.7CiteScore
2.0Impact Factor
Advanced Search
MAP  >  The Knowledge Engineering Review
2020 Volume 35
Article Contents
Next Previous
RESEARCH ARTICLE   Open Access    

Improving trust and reputation assessment with dynamic behaviour

More Information
  • 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.
  • 加载中
  • Anders , G., Seebach , H., Steghöfer , J.-P., Reif , W., André , E., Hähner , J., Müller-Schloer , C. & Ungerer , T.2016. The social concept of trust as enabler for robustness in open self-organizing systems. In Trustworthy Open Self-Organising Systems. Springer.

    Google Scholar

    Anderson , K., Lee , S. H. & Menassa , C.2013. Impact of social network type and structure on modeling normative energy use behavior interventions. Journal of Computing in Civil Engineering28(1), 30–39.

    Google Scholar

    Bifet , A. & Gavaldà , R.2007. Learning from time-changing data with adaptive windowing. In Proceedings of the 2007 SIAM International Conference on Data Mining, 443–448.

    Google Scholar

    Bifet , A. & Gavaldà , R.Adaptive learning from evolving data streams. In International Symposium on Intelligent Data Analysis. Springer.

    Google Scholar

    Bowling , M. & Veloso , M.2001. Rational and convergent learning in stochastic games. In International Joint Conference on Artificial Intelligence, 17, 1021–1026. Lawrence Erlbaum Associates Ltd.

    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

    Carmel , D. & Markovitch , S.1999. Exploration strategies for model-based learning in multi-agent systems: Exploration strategies. Autonomous Agents and Multi-Agent Systems2(2), 141–172.

    Google Scholar

    Castelfranchi , C. & Falcone , R.1998. Principles of trust for MAS: Cognitive anatomy. social importance, and quantification. In Proceedings of the International Conference on Multi Agent Systems, 1998, 72–79. IEEE.

    Google Scholar

    Chen , R., Guo , J. & Bao , F.2016. Trust management for SOA-based IoT and its applications to service composition. IEEE Transactions on Services Computing9(3), 482–495.

    Google Scholar

    D’Angelo , G., Rampone , S. & Palmieri , F.2017. Developing a trust model for pervasive computing based on apriori association rules learning and bayesian classification. Soft Computing21(21), 6297–6315.

    Google Scholar

    Debra , M., Weick , K. E. & Kramer , R. M.1995. Swift trust and temporary groups. Trust in Organizations: Frontiers of Theory and Research166.

    Google Scholar

    Fullam , K. K., Klos , T. B., Muller , G., Sabater , J., Schlosser , A., Topol , Z., Suzanne Barber , K., Rosenschein , J. S., Vercouter , L. & Voss , M.2005. A specification of the agent reputation and trust (art) testbed: experimentation and competition for trust in agent societies. In Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems, 512–518. ACM.

    Google Scholar

    Gambetta , D.2000. Can We Trust Trust?Trust: Making and Breaking Coooperative Relations, 13, 213–237.

    Google Scholar

    Griffiths , N.2006. A fuzzy approach to reasoning with trust, distrust and insufficient trust. In International Workshop on Cooperative Information Agents, 360–374. Springer.

    Google Scholar

    Hales , D. & Edmonds , B.2003. Evolving social rationality for MAS using tags. In Proceedings of the Second International Joint Conference on Autonomous Agents and Multiagent Systems, 497–503. ACM.

    Google Scholar

    Harries , M. B., Sammut , C. & Horn , K.1998. Extracting hidden context. Machine Learning32(2), 101–126.

    Google Scholar

    Hernandez-Leal , P., Taylor , M. E., Rosman , B., Enrique Sucar , L. & De Cote , E. M.2016. Identifying and tracking switching, non-stationary opponents: A Bayesian approach. In Workshops at the Thirtieth AAAI Conference on Artificial Intelligence.

    Google Scholar

    Hernandez-Leal , P., Zhan , Y., Taylor , M. E., Sucar , L. E. & de Cote , E. M.2017. Efficiently detecting switches against non-stationary opponents. Autonomous Agents and Multi-Agent Systems31(4), 767–789.

    Google Scholar

    Hoens , R. T., Polikar , R. & Chawla , N. V.2012. Learning from streaming data with concept drift and imbalance: An overview. Progress in Artifical Intelligence1(1), 89–101.

    Google Scholar

    Huynh , T. D. & Jennings , N.2004. Fire: An integrated trust and reputation model for open multi-agent systems. In ECAI 2004: 16th European Conference on Artificial Intelligence, August 22–27, 2004, Valencia, Spain: Including Prestigious Applicants [sic] of Intelligent Systems (PAIS 2004): Proceedings, 110, 18.

    Google Scholar

    Jøsang , A. & Ismail , R.2002. The beta reputation system. Proceedings of the 15th Bled Electronic Commerce Conference5, 2502–2511.

    Google Scholar

    Josang , A. & Haller , J.2007. Dirichelet reputation systems. In The Second International Conference on Availability, Reliability and Security, ARES 2007, 112–119. IEEE.

    Google Scholar

    Kamvar , S. D., Schlosser , M. T. & Garcia-Molina , H.2003. The eigentrust algorithm for reputation management in p2p networks. In Proceedings of the 12th International Conference on World Wide Web, 640–651. ACM.

    Google Scholar

    Lim Choi Keung, S. N. & Griffiths , N.2010. Trust and reputation. Agent-Based Service-Oriented Computing, 189–224.

    Google Scholar

    Klusch , M. & Gerber , A.2002. Dynamic coalition formation among rational agents. IEEE Intelligent Systems3, 42–47.

    Google Scholar

    Liang , Z. & Shi , W.2005. Pet: A personalized trust model with reputation and risk evaluation for P2P resource sharing. In Proceedings of the 38th Annual Hawaii International Conference on System Sciences.

    Google Scholar

    Liu , X., Datta , A., Rzadca , K. & Lim , E.2009. Stereotrust: A group based personalized trust model. In Proceedings of the 18th ACM Conference on Information and Knowledge Management, 7–16.

    Google Scholar

    Liu , X., Tredan , G. & Datta , A.2014. A generic trust framework for large-scale open systems using machine learning. Computational Intelligence30(4), 700–721.

    Google Scholar

    Lu , G. & Lu , J.2017. Introduction to the investigating in neural trust and multi agent systems. In Examining Information Retrieval and Image Processing Paradigms in Multidisciplinary Contexts, 269–273. IGI Global.

    Google Scholar

    Nguyen , D. T.2017. Trust Management for Complex Agent Groups. PhD thesis, Auckland University of Technology.

    Google Scholar

    Nguyen , T.D. & Bai , Q.2014. Accountable individual trust from group reputations in multi-agent systems. In Pacific Rim International Conference on Artificial Intelligence, 1063–1075.

    Google Scholar

    Nguyen , T. D. & Bai , Q.2018. A dynamic Bayesian network approach for agent group trust evaluation. Computers in Human Behavior.

    Google Scholar

    Player , C. & Griffiths , N.2017. Bootstrapping trust and stereotypes with tags. In Proceedings of the 19th International Workshop on Trust in Agent Societies (Trust at AAMAS).

    Google Scholar

    Player , C. & Griffiths , N.2018. Addressing concept drift in reputation assessment. In Proceedings of the 10th International Workshop on Adaptive Learning Agents (ALA@AAMAS).

    Google Scholar

    Press , W. H., Teukolsky , S. A., Vetterling , W. T. & Flannery , B. P.2007. Statistical description of data: Are two distributions different. Numerical Recipes: The Art of Scientific Computing, pages 730–740.

    Google Scholar

    Regan , K., Poupart , P. & Cohen , R.2006. Bayesian reputation modelling in e-marketplaces sensitive to subjectivity, deception and change. In Proceedings of the National Conference on Artificial Intelligence.

    Google Scholar

    Resnick , P., Kuwabara , K., Zeckhauser , R. & Friedman , E.2000. Reputation systems. Communications of the ACM43(12), 45–48.

    Google Scholar

    Salehi-Abari , A. & White , T.2012. Dart: A distributed analysis of reputation and trust framework. Computational Intelligence28(4), 642–682.

    Google Scholar

    Sensoy , M., Yilmaz , B. & Norman , T. J.2016. Stage: Stereotypical trust assessment through graph extraction. Computational Intelligence32(1), 72–101.

    Google Scholar

    Srivasta , M., Xiong , L. & Liu , L.2005. Trustguard: Countering vulnerabilities in reputation management for decentralized overlay networks. In Proceedings of the 14th International Conference on World Wide web, 422–431.

    Google Scholar

    Tahta , U. E., Sen , S. & Can , A. B.2015. Gentrust: A genetic trust management model for peer to peer systems. Applied Soft Computing34, 693–704.

    Google Scholar

    Taylor , P., Barakat , L., Miles , S. & Griffiths , N.2018. Reputation assessment: A review and unifying abstraction. The Knowledge Engineering Review, 33.

    Google Scholar

    Teacy , L., Patel , J., Jennings , N. & Luck , M.2006. Travos: Trust and reputation in the context of inaccurate information sources. Autonomous Agents and Multi-Agent Systems12(2), 183–198.

    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 Intelligence193, 149–185.

    Google Scholar

    Traverso , G., Cordero , C. G., Nojoumian , M., Azarderakhsh , R., Demirel , D., Habib , S. M. & Buchmann , J.2017. Evidence-based trust mechanism using clustering algorithms for distributed storage systems. IACR Cryptology ePrint Archive.

    Google Scholar

    Tsymbal , A.2004. The problem of concept drift: Definitions and related work. Computer Science Department, Trinity College Dublin106(2).

    Google Scholar

    Webb , G. I., Hyde , R., Cao , H., Nguyen , H. L. & Petitjean , F.2016. Characterizing concept drift. Data Mining and Knowledge Discovery30(4), 964–994.

    Google Scholar

    Yang , Y., Wu , X. & Zhu , X.2005. Combining proactive and reactive predictions for data streams. In Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, 710–715. ACM.

    Google Scholar

    Xiong , L. & Liu , L.2004. Peertrust: Supporting reputation-based trust for peer-to-peer electronic communities. IEEE Transactions on Knowledge and Data Engineering16(7), 843–857.

    Google Scholar

  • Cite this article

    Caroline Player, Nathan Griffiths. 2020. Improving trust and reputation assessment with dynamic behaviour. The Knowledge Engineering Review 35(1), doi: 10.1017/S0269888920000077
    Caroline Player, Nathan Griffiths. 2020. Improving trust and reputation assessment with dynamic behaviour. The Knowledge Engineering Review 35(1), doi: 10.1017/S0269888920000077
    shu
Download PDF

Article Metrics

Article views(58) PDF downloads(62)

Other Articles By Authors

RESEARCH ARTICLE   Open Access    

Improving trust and reputation assessment with dynamic behaviour

  • Received: 15 December 2018
  • Revised: 18 December 2019
  • Accepted: 05 January 2020
  • Published online: 17 June 2020
The Knowledge Engineering Review  35,  2020, 35(1)  |  Cite this article

Abstract: 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.

    HTML

    • Small-world topologies are defined as $n \times n$ lattices in the implementation and are therefore square numbers.

    Rights and permissions
    • © Cambridge University Press, 20202020Cambridge University Press
References (49)
  • About this article
    Cite this article
    Caroline Player, Nathan Griffiths. 2020. Improving trust and reputation assessment with dynamic behaviour. The Knowledge Engineering Review 35(1), doi: 10.1017/S0269888920000077
    Caroline Player, Nathan Griffiths. 2020. Improving trust and reputation assessment with dynamic behaviour. The Knowledge Engineering Review 35(1), doi: 10.1017/S0269888920000077
    shu

    • Catalog

      /

      DownLoad:  Full-Size Img  PowerPoint
      Return
      Return