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首页> 外文会议>Intelligent Agents, 2009. IA '09 >An intelligent agent approach for visual information structure generation
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An intelligent agent approach for visual information structure generation

机译：可视信息结构生成的智能代理方法

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This paper presents Cyclone, an intelligent agent based visual framework offering a means for the user to exploit, analyze and categorize unstructured information from various sources into a more structured and manageable form. The intelligent agent performs two processes, the first of which gathers the information, analyzes it and determines physical forces on visual objects which represent the information thus achieving unsupervised graph-based clustering based on lightweight metadata of the information, i.e. tags. Once an equilibrium state has been reached, the arrangement of similar information into visual fuzzy clusters and the intuitive interface of Cyclone aid the user in the process of categorization. The second process of the agent consists of monitoring the users and learning their categorization behavior in an online and nonintrusive fashion. Over time, as the derived categorization model for a particular user becomes increasingly confident, the Cyclone agent switches to an auto-categorization mode, thus automating the process of categorization for new or unassigned information and reducing the cognitive load for the user. The updated categorization model adapts the forces on the visual objects so that the visual clusters presented take into account users' behavior, combining aspects from both the unsupervised and supervised (learnt) approaches. We have conducted several multi-user experiments using real data from different application contexts in order to gain both a qualitative understanding of the user experience as well as collect quantitative data on how well the system performs, in particular, how presenting visual fuzzy clusters of the information affects a user's categorization behavior. The results illustrate that Cyclone's intelligent agent, performing clustering and categorization, coupled with an intuitive visualization interface represent an effective way of aiding users in generating a taxonomy on-the-fly and automating the process.
机译：本文介绍了Cyclone，这是一个基于智能代理的可视框架，它为用户提供了一种手段，使用户可以将来自各种来源的非结构化信息进行利用，分析和分类，以使其更具结构化和可管理性。智能代理执行两个过程，第一个过程收集信息，对其进行分析并确定代表该信息的视觉对象上的物理作用力，从而基于信息的轻量级元数据（即标签）实现无监督的基于图的聚类。一旦达到平衡状态，就将相似的信息排列到可视的模糊簇中，并通过Cyclone的直观界面帮助用户进行分类。代理的第二个过程包括监视用户并以在线和非侵入方式学习用户的分类行为。随着时间的流逝，随着对特定用户的派生分类模型变得越来越有信心，Cyclone代理切换到自动分类模式，从而使新的或未分配信息的分类过程自动化，并减轻了用户的认知负担。更新的分类模型适应了视觉对象上的作用力，从而使呈现的视觉集群将用户的行为考虑在内，并结合了无监督和受监督（学习）方法的各个方面。我们已经使用来自不同应用程序上下文的真实数据进行了多个多用户实验，以便获得对用户体验的定性理解，并收集有关系统性能的定量数据，特别是如何呈现可视化模糊聚类。信息会影响用户的分类行为。结果表明，Cyclone的智能代理执行聚类和分类，再加上直观的可视化界面，是一种帮助用户即时生成分类法并使流程自动化的有效方法。

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