A web service composition method

Abstract

The invention discloses a Web service combination method. Aiming at the problems of long time consumption and poor flexibility in the traditional service combination method, multi-Agent (intelligent body) technology, game theory and reinforcement learning method are introduced into the service combination process, and the service combination process Transformed into a Markov game process (Markov Games, or Stochastic Games). On this basis, the Q-learning method and team Markov game theory are further integrated to make it suitable for multi-agent collaborative learning environment, and the final service combination result is obtained by iterative solution. The method of the invention can effectively improve the solution speed, ensure that each agent can independently adapt to the environment and perform dynamic service combination while efficiently cooperating, and effectively improves the flexibility and adaptability of the service combination.

Description

technical field [0001] The invention belongs to the field of computers and relates to a Web service combination method. Background technique [0002] With the rapid development of the field of Web services in recent years and the rapid increase of service-oriented software development methods, Web service composition has attracted the attention of many researchers. Due to the limited capability of a single Web service, how to effectively integrate various services according to requirements and provide better services has become the core issue of Web service application integration. From the initial function drive to the later QoS (Quality of Service, Quality of Service) drive, relevant researchers have done a lot of research work in the field of Web service composition. However, due to the randomness of Web services and the uncertainty of the Internet environment, the field of Web service composition still faces some challenges, such as how to reduce the time spent in the c...

AI Technical Summary

Problems solved by technology

However, these methods have the following problems: First, the modeling strategy of these methods is still a Markov decision process in essence, but it is simply extended to each Agent, that is, an MDP model is established for each Agent, It does not take all agents into consideration through a global model; secondly, it does not take into account the possible game and cooperation relationships that each agent may encounter during the learning process, so it cannot be called a complete multi-agent collaborative reinforcement learning

However, since the goals of learning in a collaborative environment are different from those in a competitive environment, these methods cannot be solved as a general method, and they all have their own scope of application. Therefore, different multi-agent collaborative learning methods need to be designed for different scenarios

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