Abstract
We investigate a multi-agent reinforcement learning model for the optimization of Web service composition in this paper. Based on the model, a multi-agent Q-learning algorithm was proposed, where agents in a team would benefit from one another. In contrast to single-agent reinforcement-learning, our algorithm can speed up the convergence to optimal policy. In addition, it allows composite service to dynamically adjust itself to fit a varying environment, where the properties of the component services continue changing. A set of experiments is given to prove the efficiency of the analysis. The advantages and the limitations of the proposed approach are also discussed.
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
Busoniu, L., Babuska, R., De Schutter, B.: A comprehensive survey of multiagent reinforcement learning. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 38(2), 156–172 (2008)
Carman, M., Serafini, L., Traverso, P.: Web service composition as planning. In: ICAPS 2003 Workshop on Planning for Web Services, pp. 1636–1642 (2003)
Doshi, P., Goodwin, R., Akkiraju, R., Verma, K.: Dynamic workflow composition using markov decision processes. In: IEEE International Conference on Web Services, pp. 576–582. IEEE (2004)
Gao, A., Yang, D., Tang, S., Zhang, M.: Web service composition using markov decision processes. In: Fan, W., Wu, Z., Yang, J. (eds.) WAIM 2005. LNCS, vol. 3739, pp. 308–319. Springer, Heidelberg (2005)
Gonzaga, T., Bentes, C., Farias, R., de Castro, M., Garcia, A.: Using distributed-shared memory mechanisms for agents communication in a distributed system. In: Seventh International Conference on Intelligent Systems Design and Applications, ISDA 2007, pp. 39–46. IEEE (2007)
Hwang, S.Y., Lim, E.P., Lee, C.H., Chen, C.H.: Dynamic web service selection for reliable web service composition. IEEE Transactions on Services Computing 1(2), 104–116 (2008)
Kaelbling, L., Littman, M., Moore, A.: Reinforcement learning: A survey. Arxiv preprint cs/9605103 (1996)
Papazoglou, M., Georgakopoulos, D.: Service-oriented computing. Communications of the ACM 46(10), 25–28 (2003)
Sirin, E., Parsia, B., Wu, D., Hendler, J., Nau, D.: Htn planning for web service composition using shop2. Web Semantics: Science, Services and Agents on the World Wide Web 1(4), 377–396 (2004)
Sutton, R., Barto, A.: Reinforcement learning. Journal of Cognitive Neuroscience 11(1), 126–134 (1999)
Wang, H., Zhou, X., Zhou, X., Liu, W., Li, W., Bouguettaya, A.: Adaptive service composition based on reinforcement learning. In: Maglio, P.P., Weske, M., Yang, J., Fantinato, M. (eds.) ICSOC 2010. LNCS, vol. 6470, pp. 92–107. Springer, Heidelberg (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, H., Wang, X. (2013). A Novel Approach to Large-Scale Services Composition. In: Ishikawa, Y., Li, J., Wang, W., Zhang, R., Zhang, W. (eds) Web Technologies and Applications. APWeb 2013. Lecture Notes in Computer Science, vol 7808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37401-2_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-37401-2_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37400-5
Online ISBN: 978-3-642-37401-2
eBook Packages: Computer ScienceComputer Science (R0)