CAREER: Adaptive Intelligent Agents for the Internet

职业：互联网自适应智能代理

• 批准号: 9702251
• 负责人: Donald Wunsch
• 金额: $ 22.5万
• 依托单位: Texas Tech University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-07-01 至 2000-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9702251&HistoricalAwards=false
• 关键词: CAREER; Adaptive; Intelligent; Agents; Internet

This project will have two primary objectives: (1) to continue to extend the development and understanding of more advanced reinforcement learning schemes, which have demonstrated the ability to handle larger-scale problems and to mimic aspects of learning in the brain more than the conventional reinforcement learning schemes; (2) to apply advanced reinforcement learning to the development of more powerful intelligent agents for the Internet--and to use this application as a stimulus to the development of more powerful designs. For example, existing search engines for the Web are an extremely important class of agent, but also very limited in their capability because they tend to rely on simple string-mating techniques, occasionally supplemented by -and+ and -or+ operators and limited types of user-supplied clues. This work will explore a novel approach to this problem, based on a distributed system of intelligent agents which focus on different domains but learn to understand each other, in effects. If this approach is successful, it could have further implications well beyond the need for better search as such Ultimately it could contribute to the vision of converting the Internet itself into one large distributed intelligent system. This project will also include a substantial effort to apply these emerging tools in education, and the development of new courses related to this work.

该项目将有两个主要目标：（1）继续扩展更先进的强化学习方案的开发和理解，这些方案已被证明有能力处理更大规模的问题，并比传统的强化学习方案更多地模仿大脑中的学习方面;（2）将先进的强化学习应用于开发更强大的互联网智能代理，并将此应用作为开发更强大设计的刺激。 例如，现有的Web搜索引擎是一类非常重要的代理，但它们的能力也非常有限，因为它们倾向于依赖于简单的字符串匹配技术，偶尔辅以-和+和-或+运算符和有限类型的用户提供的线索。 这项工作将探索一种新的方法来解决这个问题，基于一个分布式系统的智能代理，专注于不同的领域，但学习相互理解，效果。 如果这种方法是成功的，它可能会有进一步的影响，远远超出了需要更好的搜索，最终它可以有助于将互联网本身转换成一个大型分布式智能系统的愿景。 这一项目还将包括作出重大努力，在教育中应用这些新出现的工具，并开发与这项工作有关的新课程。