RI: Medium: Collaborative Research: Solving Stochastic Planning Problems Through Principled Determinization

RI：媒介：协作研究：通过原则确定解决随机规划问题

• 批准号: 0905678
• 负责人: Alan Fern
• 金额: $ 27.88万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2013-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0905678&HistoricalAwards=false
• 关键词: RI; Medium; Collaborative; Research; Solving

Probabilistic and decision-theoretic planning, which operates under conditions of uncertainty, has important applications in science and engineering, but such stochastic methods have been under-utilized because these planners do not scale to large, complicated problems. In contrast, the past decade has seen tremendous scale-up in deterministic planning techniques, which do not directly address the same challenges of uncertainty head on. This project provides a systematic framework for exploiting this progress in deterministic planning technology - be it classical, temporal or partial satisfaction planning problems - in stochastic planning as well, by novel adaptation of theory and/or methods of determinization, hindsight optimization, and machine learning. This project is helping to bridge the traditional divide between deterministic and stochastic planning communities, and it is bringing stochastic planning technology to real-world applications. This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).

在不确定性条件下运行的概率和决策理论计划在科学和工程中具有重要的应用，但是这种随机方法的利用不足，因为这些计划者没有扩展到大型，复杂的问题。相比之下，过去十年来，确定性的计划技术的规模巨大，这并不能直接应对不确定性的挑战。该项目提供了一个系统的框架，用于利用确定性计划技术的这一进展 - 无论是经典，时间或部分满意度计划问题 - 在随机计划中，还通过新的理论和/或确定性，事后观察优化和机器学习的方法来适应。该项目有助于弥合确定性和随机规划社区之间的传统鸿沟，并将随机计划技术带入现实世界的应用。该奖项是根据2009年《美国复苏与再投资法》（公法111-5）资助的。