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Research for Providing an incentive among the Internet users to reach reliable information

为互联网用户获取可靠信息提供激励的研究

基本信息

批准号：
16500080
负责人：
ARAI Sachiyo
金额：
$ 2.3万
依托单位：
Chiba University (2005-2006)Kyoto University (2004)
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2004
资助国家：
日本
起止时间：
2004 至 2006
项目状态：
已结题

项目摘要

We have developed an infrastructure in which "interaction design" and "interoperability" are guaranteed for modeling the multi-agent domain, such as Grid computing, Electric-commerce, and Digital city. Our main interest is collaboration on the web among the agents and human beings. The difficulties are caused by the fact that information sources are distributed widely throughout the WWW environment, and each human has its preference for the particular type of information.To share and reuse knowledge of others', the agent has to learn about the agents which would provide the reliable and useful information. At the same time, extracting semantics from contents is very critical issue to make information interoperable to be shared and be reused among the heterogeneous agents. The statistical learning techniques were applicable for extracting the semantics and a text classification. While exploring in the scene of the Web, very dynamic and open environment, the agents should be adaptive and … More flexible to trawl for appropriate information which satisfy the desire of user's. We have realized adaptive aspects of agents' by reinforcement learning frameworks.Our research aims at realizing social knowledge through sharing and employing information on the Web. As a first step, we proposed a multi-agent scenarios which enables us to design interaction between users, between users and agents, and among agents, as well. By employing this translator, "interoperability" is guaranteed on the composite Web service, and consequently it becomes reusable to other users. The concepts of "interaction design" and "interoperability" work complementarily for coordinating heterogeneous agents and humans who have different motivations. This overall architecture not only enhances collaboration between users on the Web, but also facilitates user-agent interaction design, and therefore is expected to contribute to Collaborative Commerce and Digital City that will unfold in the Internet in the future. Less

我们已经开发了一个基础设施，在这个基础设施中，“交互设计”和“互操作性”被保证用于多代理领域的建模，例如网格计算、电子商务和数字城市。我们的主要兴趣是代理和人类在网络上的协作。造成这种困难的原因是信息源在WWW环境中广泛分布，每个人对特定类型的信息都有自己的偏好。为了共享和重用其他代理的知识，代理必须了解哪些代理可以提供可靠和有用的信息。同时，从内容中提取语义是使信息在异构代理之间实现互操作共享和重用的关键问题。统计学习技术适用于语义提取和文本分类。在Web这种动态开放的环境中进行搜索时，智能体需要具有较强的自适应能力，能够更灵活地搜索到满足用户需求的信息。我们通过强化学习框架实现了智能体的自适应方面。我们的研究旨在通过在网络上共享和使用信息来实现社会知识。作为第一步，我们提出了一个多智能体场景，它使我们能够设计用户之间、用户与智能体之间以及智能体之间的交互。通过使用这个转换器，复合Web服务上的“互操作性”得到了保证，因此它可以被其他用户重用。“交互设计”和“互操作性”的概念对于协调具有不同动机的异构代理和人类是互补的。这种整体架构不仅增强了Web上用户之间的协作，而且还促进了用户代理交互设计，因此有望为将来在Internet上展开的协作商业和数字城市做出贡献。少