  Artificial Intelligence (AI) has long pursued models, theories, and
techniques to imbue machines with human-like general intelligence. Yet even the
currently predominant data-driven approaches in AI seem to be lacking humans'
unique ability to solve wide ranges of problems. This situation begs the
question of the existence of principles that underlie general problem-solving
capabilities. We approach this question through the mathematical formulation of
analogies across different problems and solutions. We focus in particular on
problems that could be represented as tree-like structures. Most importantly,
we adopt a category-theoretic approach in formalising tree problems as
categories, and in proving the existence of equivalences across apparently
unrelated problem domains. We prove the existence of a functor between the
category of tree problems and the category of solutions. We also provide a
weaker version of the functor by quantifying equivalences of problem categories
using a metric on tree problems.
