Query Learning of Advice and Nominal Automata

TL;DR

This paper introduces new query learning bounds for advice and nominal automata, extending classical DFA learning results to more complex automata with advice and infinite alphabets, using combinatorial complexity measures.

Contribution

It provides the first known upper bounds for query complexity of advice DFAs and improves qualitative results for nominal DFAs using a combinatorial approach.

Findings

First known upper bounds for advice DFA query complexity

Qualitative improvements for nominal DFA learning bounds

Application of combinatorial complexity measures to new automata classes

Abstract

Learning automata by queries is a long-studied area initiated by Angluin in 1987 with the introduction of the $L^*$ algorithm to learn regular languages, with a large body of work afterwards on many different variations and generalizations of DFAs. Recently, Chase and Freitag introduced a novel approach to proving query learning bounds by computing combinatorial complexity measures for the classes in question, which they applied to the setting of DFAs to obtain qualitatively different results compared to the $L^*$ algorithm. Using this approach, we prove new query learning bounds for two generalizations of DFAs. The first setting is that of advice DFAs, which are DFAs augmented with an advice string that informs the DFA's transition behavior at each step. For advice DFAs, we give the first known upper bounds for query complexity. The second setting is that of nominal DFAs, which generalize DFAs to infinite alphabets which admit some structure via symmetries. For nominal DFAs, we make qualitative improvements over prior results.