Towards efficient selection of Web services with reinforcement learning process

As an emerging technology for implementing Web services over the Internet, mobile agent model has several advantages over the traditional RFC model. However, with the popularity of distributed networks (e.g. Internet), Web service providers tend to rely on external resources to complete certain tasks. This definitely increases the difficulty in locating appropriate service providers according to clients´ requirements in the new scenario. To address this issue, we propose a reinforcement learning process based on the mobile agent model, which makes agents more efficient and intelligent in selecting Web service providers. Finally, an implementation of our prototype is presented