Algorithms for learning finite automata from queries: a unified view

Abstract

In this survey we compare several known variants of the algorithm for learning deterministic finite automata via membership and equivalence queries. We believe that our presentation makes it easier to understand what is going on and what the differences between the various algorithms mean. We also include the comparative analysis of the algorithms, review some known lower bounds, prove a new one, and discuss the question of parallelizing this sort of algorithms.