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.

该项目是基础研究，旨在提高智能软件代理的性能，这是基于代理商的过去经验是一种有价值且普遍不足的数据库的观察。 目的是生成比现有的强化学习算法更强大地使用数据的算法，从而可以将代理商存储的体验视为可以开采以进行绩效改善信息的存储库的视图。 更一般而言，代理可以选择使用通过观察其他代理或挖掘网络获得的数据，在许多领域都可以感受到这项研究的影响。 例如，可以期望软件学习代理以更像人性化的方式学习。值得注意的经历将被记住，他们对未来表现的影响不会减弱。 数据上没有采样要求，因此可以从观看其他人那里学习，并可以使用存储数据的存储库来学习新的行为。 这项工作的可能实际应用包括网络管理和电子商务。