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

职业：智能规划和控制的决策理论方法

• 批准号: 9984952
• 负责人: Eric Hansen
• 金额: $ 25万
• 依托单位: Mississippi State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-03-01 至 2005-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9984952&HistoricalAwards=false
• 关键词: CAREER; Decision; Theoretic; Approach; Intelligent

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.

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。通过利用因子化表示形式的问题结构以及分层和分布式问题分解，这应该会产生更有效的规划算法。该项目的一个补充重点是决策理论技术的应用，实际问题，包括规划知识发现的过程中的一个大型海洋图像数据库，并在一个并行的，分布式内存环境中的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。