Dynamic Uncertainty Management for Large Scale Problem Solving

目前AL和专家系统中的不确定性管理方法在很大程度上忽略了大规模问题解决的动态性。例如，在给定静态问题模型的情况下，许多研究都集中在计算单个查询或查询集的答案的问题上。然而，在大规模问题解决方面的高级工作，如故事和图像理解，表明这种询问穿插在更大的模型规范/重新公式和证据断言流中。这项研究的目标是在这个更高的、面向任务的层面上理解不确定性管理的功能需求，并开发为这些需求提供有效支持的不确定性管理系统。

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.