Abstract
Assessing trust and reputation is essential in multi-agent systems where agents must decide who to interact with. Assessment typically relies on the direct experience of a trustor with a trustee agent, or on information from witnesses. Where direct or witness information is unavailable, such as when agent turnover is high, stereotypes learned from common traits and behaviour can provide this information. Such traits may be only partially or subjectively observed, with witnesses not observing traits of some trustees or interpreting their observations differently. Existing stereotype-based techniques are unable to account for such partial observability and subjectivity. In this paper we propose a method for extracting information from witness observations that enables stereotypes to be applied in partially and subjectively observable dynamic environments. Specifically, we present a mechanism for learning translations between observations made by trustor and witness agents with subjective interpretations of traits. We show through simulations that such translation is necessary for reliable reputation assessments in dynamic environments with partial and subjective observability.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Artz, D., Gil, Y.: A survey of trust in computer science and the semantic web. Web Semant. Sci. Serv. Agents World Wide Web 5(2), 58–71 (2007)
Burnett, C., Norman, T., Sycara, K.: Stereotypical trust and bias in dynamic multiagent systems. ACM Trans. Intell. Syst. Technol. 4(2) (2013)
Fang, H., Zhang, J., Şensoy, M., Thalmann, N.: A generalized stereotypical trust model. In: International Conference on Trust, Security and Privacy in Computing and Communications, pp. 698–705, June 2012
Frank, E., Hall, M., Witten, I.: Data Mining: Practical Machine Learning Tools and Techniques, 4th edn. Morgan Kaufmann, Burlington (2016)
Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. 46(4), 44:1–44:37 (2014)
Hendrikx, F., Bubendorfer, K., Chard, R.: Reputation systems: a survey and taxonomy. J. Parallel Distrib. Comput. 75, 184–197 (2015)
Huynh, T., Jennings, N., Shadbolt, N.: An integrated trust and reputation model for open multi-agent systems. Auton. Agents Multi-Agent Syst. 13(2), 119–154 (2006)
Jøsang, A.: A logic for uncertain probabilities. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 09(03), 279–311 (2001)
Jøsang, A., Ismail, R.: The Beta reputation system. In: Electronic Commerce Conference, pp. 41–55 (2002)
Jøsang, A., Ismail, R., Boyd, C.: A survey of trust and reputation systems for online service provision. Decis. Support Syst. 43(2), 618–644 (2007)
Koster, A., Schorlemmer, M., Sabater-Mir, J.: Engineering trust alignment: theory, method and experimentation. Int. J. Hum. Comput. Stud. 70(6), 450–473 (2012)
Liu, X., Datta, A., Rzadca, K.: Trust beyond reputation: a computational trust model based on stereotypes. Electron. Commer. Res. Appl. 12(1), 24–39 (2013)
Quinlan, R.: Learning with continuous classes. In: Australian Joint Conference on Artificial Intelligence, pp. 343–348. World Scientific, Singapore (1992)
Read, J., Reutemann, P., Pfahringer, B., Holmes, G.: MEKA: a multi-label/multi-target extension to WEKA. J. Mach. Learn. Res. 17(21), 1–5 (2016)
Regan, K., Poupart, P., Cohen, R.: Bayesian reputation modeling in e-marketplaces sensitive to subjectivity, deception and change. In: National Conference on Artificial Intelligence, vol. 2, pp. 1206–1212, July 2006
Şensoy, M., Yilmaz, B., Norman, T.: STAGE: stereotypical trust assessment through graph extraction. Computat. Intell. 32(1), 72–101 (2016)
Teacy, L., Luck, M., Rogers, A., Jennings, N.: An efficient and versatile approach to trust and reputation using hierarchical Bayesian modelling. Artif. Intell. 193, 149–185 (2012)
Teacy, L., Patel, J., Jennings, N., Luck, M.: Coping with inaccurate reputation sources: experimental analysis of a probabilistic trust model. In: International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 997–1004 (2005)
Tsoumakas, G., Katakis, I.: Multi-label classification: an overview. Int. J. Data Warehous. Min. 3(3), 64–74 (2007)
Wahab, O., Bentahar, J., Otrok, H., Mourad, A.: A survey on trust and reputation models for web services: single, composite, and communities. Decis. Support Syst. 74, 121–134 (2015)
Wang, Y., Singh, M.: Formal trust model for multiagent systems. In: International Joint Conference on Artifical Intelligence, pp. 1551–1556. Morgan Kaufmann, San Francisco, January 2007
Wang, Y., Singh, M.: Evidence-based trust: a mathematical model geared for multiagent systems. ACM Trans. Auton. Adapt. Syst. 5(4), 14:1–14:28 (2010)
Yu, H., Shen, Z., Leung, C., Miao, C., Lesser, V.: A survey of multi-agent trust management systems. IEEE Access 1, 35–50 (2013)
Zhang, J., Cohen, R.: Evaluating the trustworthiness of advice about seller agents in e-marketplaces: a personalized approach. Electron. Commer. Res. Appl. 7(3), 330–340 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Taylor, P., Griffiths, N., Barakat, L., Miles, S. (2017). Stereotype Reputation with Limited Observability. In: Sukthankar, G., Rodriguez-Aguilar, J. (eds) Autonomous Agents and Multiagent Systems. AAMAS 2017. Lecture Notes in Computer Science(), vol 10642. Springer, Cham. https://doi.org/10.1007/978-3-319-71682-4_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-71682-4_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-71681-7
Online ISBN: 978-3-319-71682-4
eBook Packages: Computer ScienceComputer Science (R0)