Web service combination method based on depth reinforcement learning

Abstract

The invention discloses a web service combination method based on depth reinforcement learning for overcoming the problems of long time consumption, poor flexibility and non-ideal combination effect of the traditional service combination method in large-scale service scenes. The depth reinforcement learning technology and the heuristic thought are applied to the service combination problem. In addition, by considering the partial observability of the real environment, the service combination process is converted into a partially-observable Markov decision process POMDP, the solution problem of the POMDP is solved by using a recurrent neural network, and the method still expresses high efficiency encountering the challenge of curse of dimensionality. By adoption of the method provided by the invention, the solution speed can be effectively improved, the dynamic service combination environment is automatically adapted on the basis of ensuring the quality of the service combination scheme, and the adaptability and the flexibility of the service combination efficiency is effectively improved in a large-scale dynamic service combination scene.

Description

Technical field [0001] The invention belongs to the computer field, and particularly relates to a Web service composition method based on deep reinforcement learning. Background technique [0002] With the continuous development of network information technology, users' functional requirements for software systems are becoming more and more diversified, complicated and varied. This trend has given birth to a new design or architecture concept for software products: Service-Oriented Architecture (SOA), which requires applications to be an independent collection of interactive services that provide good interfaces. As a new platform for establishing interoperable distributed applications, Web services are network-based, distributed, self-describing, and modular components. They perform specific tasks and follow certain technical specifications. Publish, locate and call on the Internet, thus becoming the most promising technical means to realize the SOA architecture. [0003] Nowada...

AI Technical Summary

Problems solved by technology

However, in the actual environment, the agent cannot fully perceive the environmental information. This kind of partial perception problem belongs to the non-Markovian environment. If it is not processed for the reinforcement learning algorithm, this kind of learning will not be able to converge.

(2) The theory of reinforcement learning focuses on small-scale and discrete problems, but in the real service composition problem, the scale of the service composition problem is not to be underestimated and the states are continuous

The recurrent neural network (RNN) is suitable for serialized data and can simulate these data more accurately. It records the activation value at each moment, and enhances the time correlation of the network by adding a self-connected hidden layer at cross-domain time points. , but on the contrary, this also makes it difficult to train the entire network and the phenomenon of gradient disappearance

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