Dynamic Uncertainty Management for Large Scale Problem Solving

解决大规模问题的动态不确定性管理

• 批准号: 9100530
• 负责人: Bruce D'Ambrosio
• 金额: $ 25.04万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-09-01 至 1995-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9100530&HistoricalAwards=false
• 关键词: Dynamic; Uncertainty; Management; Large; Scale

Current approaches to uncertainty management in AL and expert systems largely ignore the dynamics of large-scale problem solving. For example, much research has focused on the problem of computing the answer to a single query or set of queries, given a static problem model. However, advanced work in large scale problem solving, such as story and image understanding, reveals that such queries are interspersed in a larger stream of model specifications/reformulations and evidence assertions. This research has as its goal the understanding of the functionalrequirements for uncertainty management at this higher, task- oriented level, and the development of uncertainty management systems which provide efficient support for these requirements.

人工智能和专家系统中不确定性管理的当前方法 系统在很大程度上忽略了大规模问题的动态性 解决 例如，许多研究都集中在这个问题上， 计算单个查询或一组查询的答案， 给出一个静态问题模型。 然而，先进的工作， 规模问题解决，如故事和图像理解， 揭示了这样的查询散布在一个更大的流， 模型规范/重新表述和证据断言。 这项研究的目标是了解 不确定性管理的功能要求， 任务导向层次，发展不确定性 管理系统，为这些提供有效的支持 要求.