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

在不确定性条件下运行的概率和决策理论规划在科学和工程中具有重要的应用，但这种随机方法一直未得到充分利用，因为这些规划器不能扩展到大型复杂问题。相反，在过去的十年里，确定性规划技术已经有了巨大的发展，但并没有直接解决同样的不确定性问题。本项目提供了一个系统的框架，用于利用确定性规划技术的进步-无论是经典的，时间的还是部分满意的规划问题-在随机规划中，通过新颖的适应理论和/或确定性方法，事后优化和机器学习。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，并将随机规划技术带入现实世界的应用。该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。

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).