‘Learning from an informant’ is the setting in which the data consists of labelled strings, each label indicating whether or not the string belongs to the target language.

Of all the issues which grammatical inference scientists have worked on, this is probably the one on which most energy has been spent over the years. Algorithms have been proposed, competitions have been launched, theoretical results have been given. On one hand, the problem has been proved to be on a par with mighty theoretical computer science questions arising from combinatorics, number theory and cryptography, and on the other hand cunning heuristics and techniques employing ideas from artificial intelligence and language theory have been devised.

There would be a point in presenting this theme with a special focus on the class of context-free grammars with a hope that the theory for the particular class of the finite automata would follow, but the history and the techniques tell us otherwise. The main focus is therefore going to be on the simpler yet sufficiently rich question of learning deterministic finite automata from positive and negative examples.