RLPLA: A Reinforcement Learning Algorithm of Web Service Composition with Preference Consideration

There are many static and dynamic Web services composition strategies, however literatures about automatic composition is very rare. In this article, a new algorithm based on reinforcement learning is proposed to realize web service composition automatically and randomly. On the other hand, the existing composition prototype systems mainly focus on function-oriented composition, but not QoS-oriented composition. After understanding the function-oriented composition by reinforcement learning, this paper then introduces preference logic to seek a QoS optimization solution, which is some kind of qualitative solution. When compared with quantitative solution it has many advantages. The result is a novel algorithm RLPLA, which is an algorithm of Web services composition based on reinforcement learning and preference logic

[1]  Danilo Ardagna,et al.  Adaptive Service Composition in Flexible Processes , 2007, IEEE Transactions on Software Engineering.

[2]  He Hui,et al.  Treenet:A Web Services Composition Model Based on Spanning tree , 2007, 2007 2nd International Conference on Pervasive Computing and Applications.

[3]  H. Saba,et al.  E-Flow: A solution of Workflow for integration of the management of processes with the documentation of quality using XML , 2006, 2006 IEEE International Engineering Management Conference.

[4]  Danilo Ardagna,et al.  Global and Local QoS Guarantee in Web Service Selection , 2005, Business Process Management Workshops.

[5]  Miltiades E. Anagnostou,et al.  QoS awareness support in Web-Service semantics , 2006, Advanced Int'l Conference on Telecommunications and Int'l Conference on Internet and Web Applications and Services (AICT-ICIW'06).

[6]  Shanping Li,et al.  Constraint satisfaction in dynamic Web service composition , 2005, 16th International Workshop on Database and Expert Systems Applications (DEXA'05).

[7]  Patrick Girard,et al.  Von Wright’s preference logic reconsidered , 2006 .

[8]  Huaimin Wang,et al.  Quality driven Web services selection , 2005, IEEE International Conference on e-Business Engineering (ICEBE'05).

[9]  A.G. Barto,et al.  Reinforcement learning in the real world , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[10]  Fabio Casati,et al.  Web services interoperability specifications , 2006, Computer.

[11]  Maria Luisa Villani,et al.  QoS-aware replanning of composite Web services , 2005, IEEE International Conference on Web Services (ICWS'05).

[12]  James A. Hendler,et al.  Web Service Composition via Problem Decomposition Across Multiple Ontologies , 2007, 2007 IEEE Congress on Services (Services 2007).

[13]  Danilo Ardagna,et al.  Global and local QoS constraints guarantee in Web service selection , 2005, IEEE International Conference on Web Services (ICWS'05).

[14]  Wendy Hall,et al.  The Semantic Web Revisited , 2006, IEEE Intelligent Systems.

[15]  Liu Zhuang,et al.  Solving Fuzzy QoS Constraint Satisfaction Technique for Web Service Selection , 2007, 2007 International Conference on Computational Intelligence and Security Workshops (CISW 2007).

[16]  Giuseppe De Giacomo,et al.  Automatic Web Service Composition , 2006, 2006 IEEE International Conference on Services Computing (SCC'06).

[17]  Leon van der Torre,et al.  Algorithms for a Nonmonotonic Logic of Preferences , 2005, ECSQARU.