CARSA: A context-aware reasoning-based service agent model for AI planning of web service composition

In order to achieve automatic and more intelligent service composition, dynamic description logic (DDL) is proposed and utilized as one emerging logic-level solution. However, reasoning optimization and utilization in such DDL-related solutions is still an open problem. In this paper, we propose the context-aware reasoning-based service agent model (CARSA) which exploits the relationships among different service consumers and providers, together with the corresponding optimization approach to strengthen the effectiveness of Web service composition. Through the model, two reasoning optimization methods are proposed based on the substitute relationship and the dependency relationship, respectively, so irrelevant actions can be filtered out of the reasoning space before the DDL reasoning process is carried out. The case study and experimental analysis demonstrates the capability of the proposed approach.

► Fully exploits the relationships among different service consumers and providers through context awareness.
► Propose two logical reasoning optimization methods based on the substitute relationship and the dependency relationship, respectively.
► Execute DDL actions filtering and strengthen the effectiveness of Web service composition.