Heuristic Solution Approaches for Models of Sequential Decisionmaking Under Uncertainty and Risk

不确定性和风险下顺序决策模型的启发式求解方法

• 批准号: 8708183
• 负责人: Chelsea White
• 金额: $ 10.59万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1987
• 资助国家: 美国
• 起止时间: 1987-09-01 至 1990-08-01
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8708183&HistoricalAwards=false
• 关键词: Heuristic; Solution; Approaches; Models; Sequential

This research will investigate the usefulness of applying heuristic search algorithms, an important class of artificial intelligence problem solving techniques, to solve various types of Markov decision processes. The aim of the research is to enhance the tractability of these models of sequential decision making under uncertainty. It is anticipated that the research will produce needed extensions of a wide class of heuristic search algorithms as well as suggesting potential areas for integrating concepts in heuristic search with concepts associated with stochastic control and the decision sciences.

本研究将探讨应用启发式搜索算法（一类重要的人工智能问题解决技术）来解决各种类型的马尔可夫决策过程的有用性。研究的目的是提高这些不确定条件下序列决策模型的可追溯性。预计该研究将产生广泛的启发式搜索算法所需的扩展，并提出将启发式搜索中的概念与随机控制和决策科学相关的概念相结合的潜在领域。