Tractable Reasoning

这项研究的目的是通过理解和开发易于计算的推理任务类来设计自动推理的新方法。该方法的基本假设是，智能行为是通过利用任务环境的理想化模型来实现的。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的可处理模型的近似方法覆盖了智能活动的很大一部分，因此这种方法应该作为自动推理系统的基础。在人工智能中，一种被发现对建模和解决计算密集型任务有用的语言是约束网络及其使用。通过使用理想化的可处理模型、约束网络和其他在这一领域有用的技术，人们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。

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.