  There is currently a rapid increase in the number of challenge problem,
benchmarking datasets and algorithmic optimization tests for evaluating AI
systems. However, there does not currently exist an objective measure to
determine the complexity between these newly created domains. This lack of
cross-domain examination creates an obstacle to effectively research more
general AI systems. We propose a theory for measuring the complexity between
varied domains. This theory is then evaluated using approximations by a
population of neural network based AI systems. The approximations are compared
to other well known standards and show it meets intuitions of complexity. An
application of this measure is then demonstrated to show its effectiveness as a
tool in varied situations. The experimental results show this measure has
promise as an effective tool for aiding in the evaluation of AI systems. We
propose the future use of such a complexity metric for use in computing an AI
system's intelligence.
