  Artificial Intelligence frameworks should allow for ever more autonomous and
general systems in contrast to very narrow and restricted (human pre-defined)
domain systems, in analogy to how the brain works. Self-constructive Artificial
Intelligence ($SCAI$) is one such possible framework. We herein propose that
$SCAI$ is based on three principles of organization: self-growing,
self-experimental and self-repairing. Self-growing: the ability to autonomously
and incrementally construct structures and functionality as needed to solve
encountered (sub)problems. Self-experimental: the ability to internally
simulate, anticipate and take decisions based on these expectations.
Self-repairing: the ability to autonomously re-construct a previously
successful functionality or pattern of interaction lost from a possible
sub-component failure (damage). To implement these principles of organization,
a constructive architecture capable of evolving adaptive autonomous agents is
required. We present Schema-based learning as one such architecture capable of
incrementally constructing a myriad of internal models of three kinds:
predictive schemas, dual (inverse models) schemas and goal schemas as they are
necessary to autonomously develop increasing functionality.
  We claim that artificial systems, whether in the digital or in the physical
world, can benefit very much form this constructive architecture and should be
organized around these principles of organization. To illustrate the generality
of the proposed framework, we include several test cases in structural adaptive
navigation in artificial intelligence systems in Paper II of this series, and
resilient robot motor control in Paper III of this series. Paper IV of this
series will also include $SCAI$ for problem structural discovery in predictive
Business Intelligence.
