Efficient Induction of Finite State Automata

TL;DR

This paper presents a novel information-theoretic algorithm for efficiently inducing finite state automata from behavioral samples, significantly reducing search space and outperforming existing methods in speed and quality.

Contribution

The paper introduces a new algorithm that drastically reduces search space and improves induction quality for finite state automata compared to prior techniques.

Findings

Algorithm reduces search space by orders of magnitude.

Outperforms existing methods in run time.

Produces higher quality automata inductions.

Abstract

This paper introduces a new algorithm for the induction if complex finite state automata from samples of behavior. The algorithm is based on information theoretic principles. The algorithm reduces the search space by many orders of magnitude over what was previously thought possible. We compare the algorithm with some existing induction techniques for finite state automata and show that the algorithm is much superior in both run time and quality of inductions.