  Rule-based decision models are attractive due to their interpretability.
However, existing rule induction methods often result in long and consequently
less interpretable rule models. This problem can often be attributed to the
lack of appropriately expressive vocabulary, i.e., relevant predicates used as
literals in the decision model. Most existing rule induction algorithms presume
pre-defined literals, naturally decoupling the definition of the literals from
the rule learning phase. In contrast, we propose the Relational Rule Network
(R2N), a neural architecture that learns literals that represent a linear
relationship among numerical input features along with the rules that use them.
This approach opens the door to increasing the expressiveness of induced
decision models by coupling literal learning directly with rule learning in an
end-to-end differentiable fashion. On benchmark tasks, we show that these
learned literals are simple enough to retain interpretability, yet improve
prediction accuracy and provide sets of rules that are more concise compared to
state-of-the-art rule induction algorithms.
