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首页> 外文会议>Information Reuse and Integration, Conf, 2005. IRI -2005 IEEE International Conference on. >Exploiting similarity metrics and case-bases for knowledge sharing between case-based reasoners
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Exploiting similarity metrics and case-bases for knowledge sharing between case-based reasoners

机译：利用相似性度量和案例库在基于案例的推理者之间共享知识

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Differences in naming, structure, precision, and representation, collectively referred to as semantic heterogeneity, have long hindered efforts to share knowledge between reasoning agents. Most current techniques require the construction of an expensive, and highly formalized interlingua before any communication can have meaning between semantically heterogeneous agents. We present here a method for case-based reasoners to share knowledge without the need for a prior interlingua. Using the similarity metrics and the cases known to each agent, we demonstrate how classes in one knowledge base can be mapped into classes of another knowledge base with the help of a critic function. In the case that the critic function is mechanically realizable (e.g. a high fidelity simulation of the domain of interest), our method becomes well-suited for highly-autonomous case-based reasoners.
机译：命名，结构，精度和表示形式上的差异（统称为语义异质性）长期以来阻碍了在推理代理之间共享知识的努力。在任何通信在语义异构代理之间具有意义之前，大多数当前技术都需要构建昂贵且高度形式化的语言。我们在这里提出了一种基于案例的推理者共享知识的方法，而无需事先进行过语言交流。使用相似性度量标准和每个代理已知的案例，我们演示了如何借助批注功能将一个知识库中的类映射到另一个知识库中的类。在注释函数可以机械实现的情况下（例如对感兴趣域进行高保真模拟），我们的方法非常适合基于案例的高度自主的推理机。

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