Tractable Reasoning

易于推理

• 批准号: 9610015
• 负责人: Rina Dechter
• 金额: $ 30万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-05-15 至 2001-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9610015&HistoricalAwards=false
• 关键词: Tractable; Reasoning

The aim of this research is to devise new methods for automating reasoning via understanding and exploiting computationally tractable classes of reasoning tasks. Fundamental to the approach is the hypothesis that intelligent behavior is accomplished through utilization of idealized models of the task's environment. To explain how people perform so well on tasks that are theoretically intractable, we must assume that approximation methods based on idealized tractable models cover a significant portion of intelligent activity, and hence such methods should serve as the basis for automated reasoning systems. A language that has been found useful for modeling and solving compuationally intensive tasks in artificial intelligence is constraint networks, and use of these networks. By using idealized tractable models, constraint networks, and other techniques found useful in this area, it is hoped that real- life problemq can be solved automatically in reasonable time as well as humans solve them.

这项研究的目的是通过理解和利用计算上易于处理的推理任务类别来设计自动推理的新方法。 该方法的基础是假设智能行为是通过利用任务环境的理想模型来实现的。为了解释人们如何在理论上难以处理的任务上表现得如此出色，我们必须假设基于理想化易处理模型的近似方法涵盖了智能活动的很大一部分，因此此类方法应该作为自动推理系统的基础。 人们发现，约束网络以及这些网络的使用对于建模和解决人工智能中的计算密集型任务很有用。 通过使用理想化的易处理模型、约束网络和其他在该领域有用的技术，希望现实生活中的问题能够在合理的时间内自动解决，就像人类解决它们一样。