  We continue our analysis of volume and energy measures that are appropriate
for quantifying inductive inference systems. We extend logical depth and
conceptual jump size measures in AIT to stochastic problems, and physical
measures that involve volume and energy. We introduce a graphical model of
computational complexity that we believe to be appropriate for intelligent
machines. We show several asymptotic relations between energy, logical depth
and volume of computation for inductive inference. In particular, we arrive at
a "black-hole equation" of inductive inference, which relates energy, volume,
space, and algorithmic information for an optimal inductive inference solution.
We introduce energy-bounded algorithmic entropy. We briefly apply our ideas to
the physical limits of intelligent computation in our universe.
