  AI constructivism as inspired by Jean Piaget, described and surveyed by Frank
Guerin, and representatively implemented by Gary Drescher seeks to create
algorithms and knowledge structures that enable agents to acquire, maintain,
and apply a deep understanding of the environment through sensorimotor
interactions. This paper aims to increase awareness of constructivist AI
implementations to encourage greater progress toward enabling lifelong learning
by machines. It builds on Guerin's 2008 "Learning Like a Baby: A Survey of AI
approaches." After briefly recapitulating that survey, it summarizes subsequent
progress by the Guerin referents, numerous works not covered by Guerin (or
found in other surveys), and relevant efforts in related areas. The focus is on
knowledge representations and learning algorithms that have been used in
practice viewed through lenses of Piaget's schemas, adaptation processes, and
staged development. The paper concludes with a preview of a simple framework
for constructive AI being developed by the author that parses concepts from
sensory input and stores them in a semantic memory network linked to episodic
data. Extensive references are provided.
