Learning regaular sets from queries and counterexamples D. Angluin (1987)

Regular language learning is the canonical problem for another model, learning with membership and equivalence queries. In this model, the learner gets to ask membership queries (“Is string [x] in the language?”) and equivalence queries (“Is my hypothesis correct, and if not, what is a string on which it disagrees with the target?”). In a seminal paper, Dana Angluin (’87), who introduced the model, showed that regular languages are exactly learnable with membership and equivalence queries, and this result helped illustrate the power and relevance of this query model. [cited from https://cstheory.blogoverflow.com/2011/08/on-learning-regular-languages/]