  The solution methods used to realize artificial general intelligence (AGI)
may not contain the formalism needed to adequately model and characterize AGI.
In particular, current approaches to learning hold notions of problem domain
and problem task as fundamental precepts, but it is hardly apparent that an AGI
encountered in the wild will be discernable into a set of domain-task pairings.
Nor is it apparent that the outcomes of AGI in a system can be well expressed
in terms of domain and task, or as consequences thereof. Thus, there is both a
practical and theoretical use for meta-theories of learning which do not
express themselves explicitly in terms of solution methods. General systems
theory offers such a meta-theory. Herein, Mesarovician abstract systems theory
is used as a super-structure for learning. Abstract learning systems are
formulated. Subsequent elaboration stratifies the assumptions of learning
systems into a hierarchy and considers the hierarchy such stratification
projects onto learning theory. The presented Mesarovician abstract learning
systems theory calls back to the founding motivations of artificial
intelligence research by focusing on the thinking participants directly, in
this case, learning systems, in contrast to the contemporary focus on the
problems thinking participants solve.
