Learning Languages from Bounded Resources: The Case of the DFA and the Balls of Strings

Comparison of standard language learning paradigms (identification in the limit, query learning, Paclearning) has always been a complex question. Moreover, when to the question of converging to a target one adds computational constraints, the picture becomes even less clear: how much do queries or negative examples help? Can we find good algorithms that change their minds very little or that make very few errors? In order to approach these problems we concentrate here on two classes of languages, the topological balls of strings (for the edit distance) and the deterministic finite automata (), and (re-)visit the different learning paradigms to sustain our claims.