Abstract
In this paper, we propose a new solution to Reactive Web Service Composition, via molding with Reinforcement Learning, and introducing modified (alterable) QoS variables into the model as elements in the Markov Decision Process tuple. Moreover, we give an example of Reactive-WSC-based mobile banking, to demonstrate the intrinsic capability of the solution in question of obtaining the optimized service composition, characterized by (alterable) target QoS variable sets with optimized values. Consequently, we come to the conclusion that the solution has decent potentials in boosting customer experiences and qualities of services in Web Services, and those in applications in the whole electronic commerce and business sector.
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Zhou, J., Chen, S. (2009). Reinforcement Learning Based Web Service Compositions for Mobile Business. In: Liu, W., Luo, X., Wang, F.L., Lei, J. (eds) Web Information Systems and Mining. WISM 2009. Lecture Notes in Computer Science, vol 5854. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05250-7_55
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DOI: https://doi.org/10.1007/978-3-642-05250-7_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-05249-1
Online ISBN: 978-3-642-05250-7
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