CAREER: A Decision-Theoretic Approach to Intelligent Planning and Control

这是一个为期4年的连续奖励的第一年。本项目采用决策理论的方法来解决智能规划和控制问题，特别是具有不确定性和不完全信息的问题。本研究的一个重点是开发改进的算法，正式的马尔可夫决策过程和/或影响图的问题。采用了一种创新的方法，其中计划（或政策）表示为有限自动机，和经典的人工智能搜索和规划技术，寻找顺序计划被推广到寻找更复杂的条件计划，采取自动机的形式。这将导致更有效的规划算法，利用问题结构的形式因子表示，以及分层和分布式的问题分解。该项目的补充重点是将决策理论技术应用于实际问题，包括规划大型海洋图像数据库中的知识发现过程，以及并行、分布式内存环境中科学计算的动态负载平衡。这项研究将导致决策理论规划和控制，可以解决更大，更复杂的问题，更有效的算法。它还将导致对如何将这些算法和模型应用于实际应用的更好理解。

This is the first year of funding of a 4-year continuing award. This project adopts a decision theoretic approach to problems of intelligent planning and control, especially problems that are characterized by uncertainty and imperfect information. One focus of this research is the development of improved algorithms for problems that are formalized as Markov decision processes and/or influence diagrams. An innovative approach is adopted in which plans (or policies) are represented as finite automata, and classic AI search and planning techniques for finding sequential plans are generalized to find more complex conditional plans that take the form of automata. This should lead to more efficient planning algorithms by exploiting problem structure in the form of factored representations, as well as hierarchical and distributed problem decomposition. A complementary focus of this project is the application of decision-theoretic techniques to, practical problems, including planning the process of knowledge discovery in a large oceanographic image database, and dynamic load balancing of scientific computations in a parallel, distributed memory environment. This research will result in more efficient algorithms for decision-theoretic planning and control that can solve larger and more complex problems. It will also result in an improved understanding of how to apply these algorithms and models to practical applications.