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引用次数: 8

摘要

作者开发了一个名为Michele的群件工具包,它支持分布式开放环境中的异步组工作。它基于通信的多代理模型,并允许用户通过称为MDL(多代理描述语言)的特殊设计语言自定义界面。作者在Michele身上加入了一种学习机制。通过该机制,Michele学习用户和系统之间的交互,并将学习到的知识存储在每个用户的环境中。文档生成宏函数已添加到MDL中。使用这个宏功能,可以将协作工作视为一组文档驱动的过程。Michele在文档生成宏函数中生成新文档时,为用户的每个查询生成一个决策树。作者描述了Michele及其代理描述语言MDL。他们描述了如何在Michele内部引入一种学习机制,并解释了其实现的细节。并结合实例对该学习机制进行了评价。
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Groupware that learns
The authors developed a groupware toolkit, called Michele, which supports asynchronous group work in distributed open environments. It is based on a multi-agent model of communication and allows a user to customize the interface by a specially designed language called MDL (multi-agent description language). The authors incorporate a learning mechanism in Michele. With the mechanism, Michele learns interactions between the user and the system, and stores learned knowledge in each user's environment. A document generation macro function has been added to MDL. With this macro function, cooperative work can be viewed as a set of document-driven procedures. Michele makes a decision tree for each query to the user when making new documents within the document generation macro function. The authors describe Michele and its agent description language MDL. They describe how to introduce a learning mechanism within Michele and explain the details of its implementation. An evaluation of the learning mechanism with a real example is described.<>
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