  According to a mainstream position in contemporary cognitive science and
philosophy, the use of abstract compositional concepts is both a necessary and
a sufficient condition for the presence of genuine thought. In this article, we
show how the ability to develop and utilise abstract conceptual structures can
be achieved by a particular kind of learning agents. More specifically, we
provide and motivate a concrete operational definition of what it means for
these agents to be in possession of abstract concepts, before presenting an
explicit example of a minimal architecture that supports this capability. We
then proceed to demonstrate how the existence of abstract conceptual structures
can be operationally useful in the process of employing previously acquired
knowledge in the face of new experiences, thereby vindicating the natural
conjecture that the cognitive functions of abstraction and generalisation are
closely related.
  Keywords: concept formation, projective simulation, reinforcement learning,
transparent artificial intelligence, theory formation, explainable artificial
intelligence (XAI)
