  Cognitive map learners (CML) are a collection of separate yet collaboratively
trained single-layer artificial neural networks (matrices), which navigate an
abstract graph by learning internal representations of the node states, edge
actions, and edge action availabilities. A consequence of this atypical
segregation of information is that the CML performs near-optimal path planning
between any two graph node states. However, the CML does not learn when or why
to transition from one node to another. This work created CMLs with node states
expressed as high dimensional vectors consistent with hyperdimensional
computing (HDC), a form of symbolic machine learning (ML). This work evaluated
HDC-based CMLs as ML modules, capable of receiving external inputs and
computing output responses which are semantically meaningful for other
HDC-based modules. Several CMLs were prepared independently then repurposed to
solve the Tower of Hanoi puzzle without retraining these CMLs and without
explicit reference to their respective graph topologies. This work suggests a
template for building levels of biologically plausible cognitive abstraction
and orchestration.
