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    基于模糊Petri网的服务发现框架研究

    Service Discovery Framework Using Fuzzy Petri Net

    • 摘要: 现有的主流服务发现方法不支持模糊逻辑推理,无法处理软计算语义,从而缺乏灵活性.为了弥补人类可理解的软计算逻辑和机器能够识别的硬逻辑的差异,提出了面向服务基于模糊Petri网的Multi-Agent服务发现框架.为支持这一框架,设计了一种模糊Petri网服务描述语言,其主要特点是变迁表示一个服务或者请求;变迁的输入库所表示服务执行前应满足的条件,变迁的输出库所表示只有在所需条件满足的情况下服务才能成功执行.变迁相关的CF值(certainty factor value)表示服务消费者对Agent所提供服务的置信度.其次,提出了支持软计算语义的松弛匹配机制,并且给出了模糊松弛匹配算法;借助于本体库(类库),通过计算命题的真实度,在不精确模糊信息情况下,可以实现服务和请求的模糊松弛匹配.

       

      Abstract: The semantic Web, as presented under W3C recommendations, deals with hard semantics in the description and manipulation of crisp data. It does not have the capability to process soft semantics, but much of real world knowledge consists of uncertain or imprecise information. In order to bridge the gap between human understandable soft logic and machine-readable hard logic, a service-oriented multi-agent framework is proposed to better integrated Web services and agents. In the proposed service discovery framework, agents are classified into three types: service-agent, request-agent and broker. Two key issues in the framework are discussed: a service description language and a service matchmaking mechanism. A fuzzy Petri nets-based service description language is proposed as a specification to publish or request for a service, and transition is used to represent a service or request; input places denote preconditions expected to hold before performing the services, and output places denote effects expected to hold after performing the services. Meanwhile, through ontology's class hierarchy, a semantic-based service matchmaking is given, which can find an appropriate service for a request. Degree of truth is used to quantify the service level that the service can satisfy a request, that is, supporting loose matching.

       

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