Managing Complexity in Qualitative Physics

管理定性物理学的复杂性

• 批准号: 8902010
• 负责人: Daniel Weld
• 金额: $ 12.56万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1989
• 资助国家: 美国
• 起止时间: 1989-07-01 至 1991-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8902010&HistoricalAwards=false
• 关键词: Managing; Complexity; Qualitative; Physics

The qualitative analysis of physical systems is a central problem in artificial intelligence, but current techniques are successful only for simple models of small systems. Exponential time complexity and the ubiquity of ambiguous conclusions limit the usefulness of qualitative physics reasoning techniques when applied to large models. Effective reasoning about complex systems will require multiple models of the system that embody different simplifying assumptions appropriate for different tasks. It is therefore proposed to develop a theory of both qualitative models and the assumption on which they are based, and to test that theory by constructing a reasoning program. This reasoner will automatically determine which assumptions are appropriate, build and use the corresponding model, and finally check its conclusions. This approach should significantly speed up reasoning, decrease ambiguity, and enable automated analysis of more complex systems.

物理系统的定性分析是人工智能的核心问题，但目前的技术仅适用于小型系统的简单模型。 指数时间复杂度和普遍存在的模糊结论限制了定性物理推理技术应用于大型模型时的实用性。 对复杂系统的有效推理将需要系统的多个模型，这些模型体现了适合不同任务的不同简化假设。 因此，建议开发一种定性模型及其所基于的假设的理论，并通过构建推理程序来测试该理论。 该推理机将自动确定哪些假设是合适的，构建并使用相应的模型，最后检查其结论。 这种方法应该可以显着加快推理速度，减少歧义，并能够对更复杂的系统进行自动分析。