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Large-scale self-adaptive composite service optimization method based on multi-agent reinforced learning

A technology of reinforcement learning and service combination, applied in the direction of electrical components, transmission systems, etc., can solve unrealizable problems, achieve effective monitoring and management, and solve the effects of uncertain and unpredictable environmental changes

Inactive Publication Date: 2013-08-14
SOUTHEAST UNIV
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Problems solved by technology

But this method needs to know the environment model of state transition probability and reward value function
This is usually not achievable in the real world

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  • Large-scale self-adaptive composite service optimization method based on multi-agent reinforced learning
  • Large-scale self-adaptive composite service optimization method based on multi-agent reinforced learning
  • Large-scale self-adaptive composite service optimization method based on multi-agent reinforced learning

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Embodiment Construction

[0034] The present invention will be described in detail below in conjunction with the accompanying drawings and examples.

[0035] The large-scale service combination optimization method based on multi-agent reinforcement learning of the present invention, the specific process is as follows Figure 4 shown, including the following steps:

[0036] 1) if figure 1 As shown, the environment of Web service composition is modeled as a Web service composition Markov decision process state transition diagram (WSC-MDP). It can be modeled by hand or by artificial intelligence planning methods. It is a 6-tuple WSC-MDP=0 ,s t , A(s), P, R>, S: is a series of atomic actions from a specific initial state s 0 The set of attainable states from which to start execution. the s 0 Represents the initial state, which represents the state when the action has not yet occurred, that is, the initial value of the workflow. the s t The target state of the user, that is, the final state of the w...

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Abstract

The invention discloses a self-adaptive composite service optimization method based on multi-agent reinforced learning. The method combines conceptions of the reinforced learning and agents, and defines the state set of reinforced learning to be the precondition and postcondition of the service, and the action set to be the Web service; parameters for Q learning including the learning rate, discount factors and Q value in reinforced learning are initialized; each agent is used for performing one composite optimizing task, and can perceive the current state, and select the optimal action under the current state as per the action selection strategy; the Q value is calculated and updated as per the Q learning algorithm; before the Q value is converged, the next round learning is performed after one learning round is finished, and finally the optimal strategy is obtained. According to the method, the corresponding self-adaptive action strategy is worked out on line as per the environment change at the time, so that higher flexibility, self-adaptability and practical value are realized.

Description

technical field [0001] The invention belongs to the field of artificial intelligence and relates to a method for self-adaptive optimization of Web service composition by using a computer. Background technique [0002] Facing the complex and changeable market environment and fierce competition, enterprises urgently need the support of application integration and e-commerce technology in order to improve their competitiveness and adaptability in the market. Due to the characteristics of Web services, it is very suitable for the integration of cross-enterprise business applications. Both the industry and academia hope to create new service functions by combining existing Web services. In order to realize application interoperability and application integration of information systems among enterprises, a service-oriented framework system can be established by encapsulating enterprise application systems with Web services, providing a Web access interface, and integrating applica...

Claims

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Application Information

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IPC IPC(8): H04L29/08
Inventor 王红兵王晓珺
Owner SOUTHEAST UNIV
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