Prediction and Planning: Bridging the Gap

预测和规划：弥合差距

• 批准号: 0209088
• 负责人: Ronald Parr
• 金额: $ 29.17万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-01 至 2006-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0209088&HistoricalAwards=false
• 关键词: Prediction; Planning; Bridging; Gap

This project is fundamental research to improve the performance of intelligent software agents, based on the observation that an agent's past experiences are a valuable and generally underutilized database. The goal is to produce algorithms that make stronger use of data than existing reinforcement learning algorithms, enabling a view of the agent's stored experiences as a repository that can be mined for performance-improving information. More generally, the agent may choose to use data obtained by observing other agents, or even from mining the web.The impact of this research may be felt in many areas. For example, software learning agents can be expected to learn in a much more human-like manner; noteworthy experiences will be remembered, and their influence on future performance will not attenuate. There will be no sampling requirements on the data, so it will be possible to learn from watching others and possible to use repositories of stored data to learn new behaviors. Among the likely practical applications of this work are network management and electronic commerce.

该项目是提高智能软件代理性能的基础研究，基于对代理过去经验是一个有价值且通常未被充分利用的数据库的观察。我们的目标是产生比现有强化学习算法更能利用数据的算法，使智能体存储经验的视图成为一个存储库，可以挖掘性能改进信息。更一般地说，代理可以选择使用通过观察其他代理获得的数据，甚至是通过挖掘web获得的数据。这项研究的影响可以在许多领域感受到。例如，可以期望软件学习代理以更像人类的方式学习；值得注意的经验会被记住，它们对未来表现的影响不会减弱。对数据没有抽样要求，因此可以通过观察他人来学习，也可以使用存储的数据库来学习新的行为。这项工作可能的实际应用包括网络管理和电子商务。