Certified reputation: how an agent can trust a stranger

ABSTRACT

Current computational trust models are usually built either on an agent's direct experience of an interaction partner (interaction trust) or reports provided by third parties about their experiences with a partner (witness reputation). However, both of these approaches have their limitations. Models using direct experience often result in poor performance until an agent has had a sufficient number of interactions to build up a reliable picture of a particular partner and witness reports rely on self-interested agents being willing to freely share their experience. To this end, this paper presents Certified Reputation (CR), a novel model of trust that can overcome these limitations. Specifically, CR works by allowing agents to actively provide third-party references about their previous performance as a means of building up the trust in them of their potential interaction partners. By so doing, trust relationships can quickly be established with very little cost to the involved parties. Here we empirically evaluate CR and show that it helps agents pick better interaction partners more quickly than models that do not incorporate this form of trust.

REFERENCES

• 1: Paul R. Cohen, Empirical methods for artificial intelligence, MIT Press, Cambridge, MA, 1995
• 2: D. Gambetta. Trust: Making and Breaking Cooperative Relations. Dept. of Sociology, University of Oxford, 2000.
• 3: T. Grandison and M. Sloman. A survey of trust in internet applications. IEEE Comm Surveys & Tutorials, 3(4), 2000.
• 4: T. D. Huynh, N. R. Jennings, and N. R. Shadbolt. On handling inaccurate witness reports. In Proc. 8th Int. Workshop on Trust in Agent Societies, pages 63--77, 2005.
• 5: Trung Dong Huynh , Nicholas R. Jennings , Nigel R. Shadbolt, An integrated trust and reputation model for open multi-agent systems, Autonomous Agents and Multi-Agent Systems, v.13 n.2, p.119-154, September 2006 [doi>10.1007/s10458-005-6825-4]
• 6: E. Michael Maximilien , Munindar P. Singh, Reputation and endorsement for web services, ACM SIGecom Exchanges, v.3 n.1, p.24-31, Winter, 2002 [doi>10.1145/844331.844335]
• 7: Sarvapali D. Ramchurn , Dong Huynh , Nicholas R. Jennings, Trust in multi-agent systems, The Knowledge Engineering Review, v.19 n.1, p.1-25, March 2004 [doi>10.1017/S0269888904000116]
• 8: J. Sabater. Trust and Reputation for Agent Societies. PhD thesis, Universitat Autònoma de Barcelona, 2003.
• 9: Halvard Skogsrud , Boualem Benatallah , Fabio Casati, Model-Driven Trust Negotiation for Web Services, IEEE Internet Computing, v.7 n.6, p.45-52, November 2003 [doi>10.1109/MIC.2003.1250583]
• 10: W. T. Luke Teacy , Jigar Patel , Nicholas R. Jennings , Michael Luck, Coping with inaccurate reputation sources: experimental analysis of a probabilistic trust model, Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems, July 25-29, 2005, The Netherlands [doi>10.1145/1082473.1082624]
• 11: A. Whitby, A. Jøsang, and J. Indulska. Filtering out unfair ratings in bayesian reputation systems. In Proc. 7th Int Workshop on Trust in Agent Societies, 2004.
• 12: Bin Yu , Munindar P. Singh, Detecting deception in reputation management, Proceedings of the second international joint conference on Autonomous agents and multiagent systems, July 14-18, 2003, Melbourne, Australia [doi>10.1145/860575.860588]
• 13: G. Zacharia and P. Maes. Trust management through reputation mechanisms. Applied Artificial Intelligence, 14(9):881--908, 2000.
• 14: Philip R. Zimmermann, The official PGP user's guide, MIT Press, Cambridge, MA, 1995

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.11 Distributed Artificial Intelligence
            Subjects: Multiagent systems

General Terms:
Design, Experimentation, Reliability

Keywords:
multi-agent systems, reputation, trust