Abstract
With the abundance of services available in today’s world, identifying those of high quality is becoming increasingly difficult. Reputation systems can offer generic recommendations by aggregating user provided opinions about service quality, however, are prone to “ballot stuffing” and “badmouthing”. In general, unfair ratings may degrade the trustworthiness of reputation systems, and changes in service quality over time render previous ratings unreliable.
In this paper, we provide a novel solution to the above problems based on Learning Automata (LA), which can learn the optimal action when operating in unknown stochastic environments. Furthermore, they combine rapid and accurate convergence with low computational complexity. In additional to its computational simplicity, unlike most reported approaches, our scheme does not require prior knowledge of the degree of any of the above mentioned problems with reputation systems. Instead, it gradually learns which users provide fair ratings, and which users provide unfair ratings, even when users unintentionally make mistakes.
Comprehensive empirical results show that our LA based scheme efficiently handles any degree of unfair ratings (as long as ratings are binary). Furthermore, if the quality of services and/or the trustworthiness of users change, our scheme is able to robustly track such changes over time. Finally, the scheme is ideal for decentralized processing. Accordingly, we believe that our LA based scheme forms a promising basis for improving the performance of reputation systems in general.
This work was partially supported by NSERC, the Natural Sciences and Engineering Research Council of Canada.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Dellarocas, C.: Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior. In: Proceedings of the 2nd ACM conference on Electronic commerce, Minneapolis, Minnesota, United States, pp. 150–157. ACM, New York (2000)
Sen, S., Sajja, N.: Robustness of reputation-based trust: boolean case. In: Proceedings of the first international joint conference on Autonomous agents and multiagent systems, part 1, Bologna, Italy, pp. 288–293. ACM, New York (2002)
Zacharia, G., Maes, P.: Trust management through reputation mechanisms. Applied Artificial Intelligence 14(9), 881–907 (2000)
Whitby, A., Jsang, A., Indulska, J.: Filtering out unfair ratings in bayesian reputation systems. In: Proceedings of the 7th Int. Workshop on Trust in Agent Societies (at AAMAS 2004). ACM, New York (2004)
Despotovic, Z., Aberer, K.: A probabilistic approach to predict peers performance in P2P networks. In: Cooperative Information Agents VIII, pp. 62–76 (2004)
Narendra, K.S., Thathachar, M.A.L.: Learning Automata: An Introduction. Prentice-Hall, New Jersey (1989)
Oommen, B.J., Ma, D.C.Y.: Stochastic automata solutions to the object partitioning problem. The Computer Journal 35, A105–A120 (1992)
Mayur Datar, M., Gionis, A., Indyk, P., Motwani, R.: Maintaining stream statistics over sliding windows. SIAM J. Comput. 31(6), 1794–1813 (2002)
Shapiro, C.: Consumer information, product quality, and seller reputation. The Bell Journal of Economics 13(1), 20–35 (1982)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yazidi, A., Granmo, OC., Oommen, B.J. (2010). A Learning Automata Based Solution to Service Selection in Stochastic Environments. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6098. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13033-5_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-13033-5_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13032-8
Online ISBN: 978-3-642-13033-5
eBook Packages: Computer ScienceComputer Science (R0)