Universal. Universal intelligence is in no way anthropocentric. Practical. In its current form the definition cannot be directly turned into a test of intelligence as the Kolmogorov complexity function is not computable. Thus in its pure form we can only use it to analyse the nature of intelligence and to theoretically examine the intelligence of mathematically defined learning algorithms.

In order to use universal intelligence more generally we will need to construct a workable test that approximates an agent's Υ value. The equation for Υ suggests how we might approach this problem. Essentially, an agent's universal intelligence is a weighted sum of its performance over the space of all environments. Thus, we could randomly generate programs that describe environmental probability measures and then test the agent's performance against each of these environments. After sampling sufficiently many environments the agent's approximate universal intelligence would be computed by weighting its score in each environment according to the complexity of the environment as given by the length of its program. Another possibility might to be try to approximate the sum by enumerating environmental programs from short to long, as the short ones will contribute by far the greatest to the sum. However in this case we will need to be able to reset the state of the agent so that it cannot cheat by learning our environmental enumeration method. In any case, various practical challenges will need to be addressed before universal intelligence can be used to construct an effective intelligence test. As this would be a significant project in its own right, in this paper we focus on the theoretical issues surrounding the universal intelligence.

## 4 Definitions And Tests Of Machine Intelligence

In this section we will survey both definitions and tests of machine intelligence. We begin with a sample of informal definitions of machine intelligence. A comprehensive survey of informal definitions is practically impossible as they often appear buried deep in articles or books. Nevertheless we have attempted to collect as many as possible and present a sample of some of the more common perspectives that have been taken.

We then survey formal definitions and tests of machine intelligence. As we will see, it is not always clear whether a proposal is a test, a formal definition, or something in between. In some cases the authors claim it is one or the other, and in some cases both. More accurately there is a spectrum of possibilities and thus we will not attempt to artificially divide them into either tests or formal definitions.

To the best of our knowledge, this section is the only general survey of tests and definitions of machine intelligence. This is remarkable given that the definition and measurement of intelligence in machines are two of the most fundamental questions in artificial intelligence. Currently most text books say very little about intelligence, other than mentioning the Turing test. We hope that our short survey will help to raise awareness of the many other proposals.