Artificial general intelligence (AGI) refers to research aimed at tackling
the full problem of artificial intelligence, that is, create truly intelligent
agents. This sets it apart from most AI research which aims at solving
relatively narrow domains, such as character recognition, motion planning, or
increasing player satisfaction in games. But how do we know when an agent is
truly intelligent? A common point of reference in the AGI community is Legg and
Hutter's formal definition of universal intelligence, which has the appeal of
simplicity and generality but is unfortunately incomputable. Games of various
kinds are commonly used as benchmarks for "narrow" AI research, as they are
considered to have many important properties. We argue that many of these
properties carry over to the testing of general intelligence as well. We then
sketch how such testing could practically be carried out. The central part of
this sketch is an extension of universal intelligence to deal with finite time,
and the use of sampling of the space of games expressed in a suitably biased
game description language.

作者讨论了如何测试普遍人工智能的智能程度，并提出了用游戏描述语言中游戏空间的抽样来扩展通用智能的概念以处理有限时间的中心思想。