Preference-Aware Web Service Composition by Reinforcement Learning

The existing composition approaches focus on either function-oriented composition or QoS-oriented composition. To our best knowledge, there is not a complete solution. Furthermore, existing solutions for QoS-oriented composition are basically a quantitative method. In many domains it is desirable to assess such QoS in a qualitative rather than quantitative way. So we propose a new algorithm, which can implement automatic composition, considering both function and QoS. Moreover this is a qualitative solution. The algorithm is based on reinforcement learning and preference logic reasoning. The theoretical proof and the experiments demonstrate the feasibility and effectiveness of the approach.