CAREER: Planning Under Uncertainty in Large Domains

该研究项目推进了人工智能在规划和强化学习领域的研究。该研究的主要目标是创建理论上合理的规划算法，该算法广泛适用于机器人导航和控制、医疗决策、柔性制造、通信网络监控和空间任务调度等任务。该研究采用双管齐下的方法来开发算法，以解决源自实际世界问题的大规模领域：仔细的形式分析，以帮助构建和识别合理的算法，以及算法的开发和密集的经验验证。这种策略鼓励算法的创建，这将有助于解决实际任务，同时避免“过度拟合”（即，通过将算法剪裁为特定领域的属性而失去通用性）。在本研究过程中开发的新规划算法结合了规划和强化学习领域的见解，使解决比以前更大、更困难的问题成为可能。该研究正在创造近似解决大型控制问题的方法，这些方法可用于从电梯设计到计算机系统的自适应控制等工程应用；这将有助于创建更高效、更安全、更可靠的系统。

This research project advances artificial-intelligence research in the areas of planning and reinforcement learning. The primary goal of the research is to create theoretically justified planning algorithms with wide applicability to tasks including robot navigation and control, medical decision making, flexible manufacturing, communications-network monitoring, and space mission scheduling. The research takes a two-pronged approach to develop algorithms for solving large-scale domains derived from practical, real-world problems: careful formal analysis to aid in the construction and identification of justifiable algorithms, and the development and intensive empirical validation of algorithms. This strategy encourages the creation of algorithms that will be useful in solving practical tasks while avoiding ``over fitting'' (i.e., losing generality by tailoring algorithms to attributes of specific domains). New planning algorithms developed in the course of this research combine insights from the areas of planning and reinforcement learning to make it possible to solve larger and more difficult problems than could be addressed previously. The research is creating methods for approximately solving large control problems that could be used in engineering applications ranging from elevator design to the adaptive control of computer systems; this will help create systems that aremore efficient, safe, and reliable.