Title :
RLPLA: A Reinforcement Learning Algorithm of Web Service Composition with Preference Consideration
Author :
Wang, Hongbing ; Tang, Pingping ; Hung, Patrick
Abstract :
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
Keywords :
Web services; formal logic; learning (artificial intelligence); optimisation; quality of service; QoS optimization solution; RLPLA; Web service composition; function-oriented composition; preference logic; reinforcement learning algorithm; Artificial intelligence; Computer science; Flowcharts; Information technology; Learning; Logic; Prototypes; Search engines; Specification languages; Web services; Web services; algorithm; reinforcement learning;
Conference_Titel :
Congress on Services Part II, 2008. SERVICES-2. IEEE
Conference_Location :
Beijing
Print_ISBN :
978-0-7695-3313-1
Electronic_ISBN :
978-0-7695-3313-1
DOI :
10.1109/SERVICES-2.2008.15