  In 2011, Hibbard suggested an intelligence measure for agents who compete in
an adversarial sequence prediction game. We argue that Hibbard's idea should
actually be considered as two separate ideas: first, that the intelligence of
such agents can be measured based on the growth rates of the runtimes of the
competitors that they defeat; and second, one specific (somewhat arbitrary)
method for measuring said growth rates. Whereas Hibbard's intelligence measure
is based on the latter growth-rate-measuring method, we survey other methods
for measuring function growth rates, and exhibit the resulting Hibbard-like
intelligence measures and taxonomies. Of particular interest, we obtain
intelligence taxonomies based on Big-O and Big-Theta notation systems, which
taxonomies are novel in that they challenge conventional notions of what an
intelligence measure should look like. We discuss how intelligence measurement
of sequence predictors can indirectly serve as intelligence measurement for
agents with Artificial General Intelligence (AGIs).
