A (hopefully) Unbiased Universal Environment Class for Measuring Intelligence of Biological and Artificial Systems

Abstract

The measurement of intelligence is usually associated with the performance over a selection of tasks or environments. The most general approach in this line is called Universal Intelligence, which assigns a probability to each possible environment according to several constructs derived from Kolmogorov complexity. In this context, new testing paradigms are being defined in order to devise intelligence tests which are anytime and universal: valid for both artificial intelligent systems and biological systems, of any intelligence degree and of any speed. In this paper, we address one of the pieces in this puzzle: the definition of a general, unbiased, universal class of environments such that they are appropriate for intelligence tests. By appropriate we mean that the environments are discriminative and that they can be feasibly built, in such a way that the environments can be automatically generated and their complexity can be computed.