Context-aware services composition based on AI planning
AI planning technologies has proven to be useful for services composition. By treating service as an action, planners do various sorts of reasoning about how to combine services into a plan. However, planners typically support only limited reasoning capabilities which cannot handle the enormous size of the data involved in the planning process over Web. In parallel, the field of context-aware computing has been focusing on providing information that can be used to characterize the situation of an entity, and thereby using context can filter the inappropriate candidate services and adapt to user's preference. The major technical contributions of this paper are: (1) We propose an OWL-SC model for the context-aware composition of Web services.$(2)$ We propose contextaware plan architecture and thus is more scalability and flexibility for the planning process, and thereby improving the efficiency and precision. (3) We propose a hybrid approach to build a plan corresponding to a context-aware service composition, based on global planning and local optimization, considering both the usability and adoption. solution.
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