Computational Rationality

在智能行为的计算研究中，有几种现象被分离出来，其中最重要的是学习、推理和规划。 已经发现普遍存在的障碍是，各种现象的形式化往往彼此不一致，甚至更常见的是，它们在计算上是难以处理的。 这个研究项目的主旨是开发一个计算框架和一些算法，将包括一个统一的方法来解决这个问题。 建议的出发点是，可能近似正确（PAC）学习的概念提供了一个系统的行为性能的定量标准的想法。 这些分析方法用于计算学习理论， 扩展到更广泛的现象类别，包括各种 推理和计划的概念。

In computational studies of intelligent behavior several phenomena have been isolated, among the most important of which are learning, reasoning, and planning. Obstacles that have been found pervasive are that the formalization of the various phenomena are often inconsistent with each other, and, even more often, that they are computationally intractable. This main thrust of this research project is to develop a computational framework and some algorithms that would comprise a unified approach to this problem area. The proposed starting point is the idea that the concept of probably approximately correct (PAC) learning provides a quantitative criterion on the behavioral performance of a system. These analytic methods used in computational learning theory, are extended to a wider class of phenomena, including various notions of reasoning and planning.