NSF Workshop: Bridging the Gap Between Operations Research and Approximate Dynamic Programming, April 16-20, 2005, in Cancun, Mexico

NSF 研讨会：弥合运筹学与近似动态规划之间的差距，2005 年 4 月 16 日至 20 日，墨西哥坎昆

• 批准号: 0548304
• 负责人: Warren Powell
• 金额: $ 4万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-01-01 至 2007-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0548304&HistoricalAwards=false
• 关键词: NSF; Workshop; Bridging; Gap; Between

Dynamic programming has long held the promise of solving some of the more challenging decision problems. However, these approaches have suffered from the well known "curse of dimensionality" which essentially limits their applicability to decision problems with a few state variables. Recent advances in approximating these decision problems have led to methods known as approximate dynamic programming. These methods are intimately related to several other methods such as linear programming, and stochastic programming. This workshop will explore connections among these methods, as well as several important applications arising in transportation, power systems, and others. This workshop will bring together researchers and students from several disciplines, such as artificial intelligence, neural networks, and operations research to focus on new breakthroughs in approximate dynamic programming, and related areas.

长期以来，动态规划一直有望解决一些更具挑战性的决策问题。然而，这些方法遭受了众所周知的“维数诅咒”的困扰，这从本质上限制了它们对具有少量状态变量的决策问题的适用性。最近在逼近这些决策问题方面的进展导致了被称为近似动态规划的方法。这些方法与其他一些方法密切相关，如线性规划和随机规划。本次研讨会将探讨这些方法之间的联系，以及在交通、电力系统和其他方面出现的几个重要应用。本次研讨会将汇集来自多个学科的研究人员和学生，如人工智能、神经网络和运筹学，重点关注近似动态规划和相关领域的新突破。