DATA-DRIVEN INDUCTIVE INFERENCE OF FINITE-STATE AUTOMATA

Abstract

Within the field of structural pattern analysis, algorithms for inference of discrete mathematical models from samples are an important area of research. This paper gives an extensive survey of state-of-the-art methods for data-driven inductive inference of finite-state automata. In addition to providing notationally consistent descriptions of the methods’ fundamental mode of operation, aspects such as sequential learning, advantages and disadvantages, and the extension to stochastic automata are also addressed.