Computational Models of Uncertainty Management and Decision Making

一个完整的不确定性管理方法需要支持交互式和增量问题制定、推理、假设排序和决策。目前的方法主要集中在推理上，对不确定性推理中的大问题提供很少或根本没有支持。本提案提出了一个计划，用于研究使用不确定性的多种表示作为资源的完整计算基础的基础。有限推理和决策。在不确定条件下制造。研究分为两个阶段。第一阶段是继续开发用于“小”的组合符号/数字表示。规模问题模型的制定与评价。这个框架提供了支持增量模型构建、评估和修订的基本功能，但是没有解决资源边界的问题，这在使用large进行推理时变得至关重要。规模 模型。拟议研究的第二阶段将解决小公司难以解决的问题。比例模型方法，通过开发表示和评估策略来分解一个大的。比例模型成一组小的。规模 模型。

A Complete approach to uncertainty management requires support for interactive and incremental problem formulation, inference, hypothesis ranking, and decision making. Current approaches concentrate primarily on inference, and provide little or no support for the larger issues in reasoning under uncertainty. This proposal presents a plan for research into the use of multiple representations of uncertainty as the basis of a complete computational foundation for resource.bounded reasoning and decision.making under uncertainty. The research is organized into two phases. The first phase is continued development of a combined symbolic/numeric representation for small.scale problem model formulation and evaluation. This framework provides basic capabilities to support incremental model construction, evaluation, and revision, but does not address issues of resource bounding which become critical in reasoning with large.scale models. A second phase of the proposed research will address problems which are intractable for the small.scale model approach, by developing representations and evaluation strategies for decomposing a large.scale model into a set of small.scale models.