A Service Composition Approach Combining Gaussian Processes and Reinforcement Learning

A technology of reinforcement learning and service combination, applied in digital transmission systems, data exchange networks, electrical components, etc., can solve the problems of lack of generalization ability and inaccurate learning results, and achieve the effect of accurate prediction and good generalization ability

Active Publication Date: 2020-04-24
SOUTHEAST UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In the traditional reinforcement learning algorithm Q-learning, the Q value is recorded through the value table, which lacks generalization ability, and the learning results are not accurate enough, and are greatly affected by noise.

Method used

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  • A Service Composition Approach Combining Gaussian Processes and Reinforcement Learning
  • A Service Composition Approach Combining Gaussian Processes and Reinforcement Learning
  • A Service Composition Approach Combining Gaussian Processes and Reinforcement Learning

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Embodiment Construction

[0034] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments.

[0035] The basic model of service composition is as figure 1 As shown, a complex software system can be regarded as a workflow composed of multiple components or subsystems. In the field of service composition, components are web services. Therefore, when performing service composition, user requirements can be modeled with an abstract task workflow diagram, in which each component is an abstract service. For each abstract service, there may be multiple candidate services, these services have similar functions, but have different QoS (Quality of Service), so the appropriate specific service can be selected from the candidate services based on the QoS attributes, and finally the available Service composition system.

[0036] The service combination method combining Gaussian process and reinforcement learning disclosed by the present invention ...

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Abstract

The invention discloses a service combination method combining a Gaussian process with reinforcement learning. The method comprises the following steps: 1, modeling a service combination problem as a tetrad Markov decision process; 2, solving the tetrad Markov decision process by using a reinforcement learning method based on Q-learning to obtain an optimal strategy, wherein a Q value Gaussian prediction model is established to update a Q value; and 3, mapping the optimal strategy into working flow of web service combination. According to the method, the learning of the Q value is modeled by using the Gaussian process, so that the accuracy and universality are better.

Description

technical field [0001] The invention relates to a method for combining Web services by using a computer, which belongs to the field of artificial intelligence. Background technique [0002] With the development of computer technology, the requirements of software systems are becoming more and more complex and changeable. Coupled with the development of the Internet and information technology, a service-oriented software architecture (Service-Oriented Architecture) has gradually emerged: it will realize a certain Some functional software or components are placed in the Internet environment as web services, and users can communicate with web services through a certain message protocol to use their functions. Finally, by combining multiple web services, a new software system that meets the needs is constructed. At present, common web services include weather services, map positioning services, and so on. [0003] For a certain function, there are generally multiple services p...

Claims

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Application Information

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Patent Type & AuthorityPatents(China)
IPC IPC(8): H04L12/24H04L29/08
CPCH04L41/145H04L41/50H04L67/02
Inventor王红兵李佳杰
OwnerSOUTHEAST UNIV