Computational Rationality

在对智能行为的计算研究中，有几种现象是孤立的，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，更经常的是，它们在计算上是难以处理的。这项研究项目的主要目的是开发一个计算框架和一些算法，这些算法将组成这个问题领域的统一方法。提出的出发点是这样一个想法，即可能近似正确(PAC)学习的概念为系统的行为性能提供了一个量化标准。计算学习理论中使用的这些分析方法被扩展到更广泛的现象类别，包括推理和规划的各种概念。

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.