Memory As A Monadic Control Construct In Problem-Solving
  Recent advances in programming languages study and design have established a
standard way of grounding computational systems representation in category
theory. These formal results led to a better understanding of issues of control
and side-effects in functional and imperative languages. This framework can be
successfully applied to the investigation of the performance of Artificial
Intelligence (AI) inference and cognitive systems. In this paper, we delineate
a categorical formalisation of memory as a control structure driving
performance in inference systems. Abstracting away control mechanisms from
three widely used representations of memory in cognitive systems (scripts,
production rules and clusters) we explain how categorical triples capture the
interaction between learning and problem-solving.
