摘要翻译：
人工智能的一个基本问题是，没有人真正知道智能是什么。当我们需要考虑与人类有显著不同的人工系统时，这个问题尤其尖锐。在本文中，我们从以下几个方面来探讨这个问题：我们采用了一些专家给出的关于人类智力的非正式定义，并提取了它们的基本特征。然后在数学上对这些进行形式化处理，以产生任意机器智能的一般度量。我们相信，这个方程形式上抓住了最广泛合理意义上的机器智能概念。然后我们展示了这个形式定义是如何与普遍最优学习代理理论相关联的。最后，我们回顾了许多其他的测试和已经提出的机器智能的定义。
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英文标题：
《Universal Intelligence: A Definition of Machine Intelligence》
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作者：
Shane Legg and Marcus Hutter
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最新提交年份：
2007
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: We take a number of well known informal definitions of human intelligence that have been given by experts, and extract their essential features. These are then mathematically formalised to produce a general measure of intelligence for arbitrary machines. We believe that this equation formally captures the concept of machine intelligence in the broadest reasonable sense. We then show how this formal definition is related to the theory of universal optimal learning agents. Finally, we survey the many other tests and definitions of intelligence that have been proposed for machines.
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PDF链接：
https://arxiv.org/pdf/0712.3329