  Automaton models are often seen as interpretable models. Interpretability
itself is not well defined: it remains unclear what interpretability means
without first explicitly specifying objectives or desired attributes. In this
paper, we identify the key properties used to interpret automata and propose a
modification of a state-merging approach to learn variants of finite state
automata. We apply the approach to problems beyond typical grammar inference
tasks. Additionally, we cover several use-cases for prediction, classification,
and clustering on sequential data in both supervised and unsupervised scenarios
to show how the identified key properties are applicable in a wide range of
contexts.
