Efficient Learning of Weak Deterministic B\"uchi Automata

TL;DR

This paper introduces an efficient learning algorithm for weak deterministic B"uchi automata that significantly reduces the number of queries needed compared to previous methods, enabling faster automaton learning.

Contribution

It presents a novel Angluin-style learning algorithm tailored for minimal normal forms of wDBAs, improving query complexity from quintic to quadratic.

Findings

Fewer queries needed for learning wDBAs compared to previous methods

Algorithm effectively learns minimal normal forms of wDBAs

Benchmark results confirm theoretical improvements in query efficiency

Abstract

We present an efficient Angluin-style learning algorithm for weak deterministic B\"uchi automata (wDBAs). Different to ordinary deterministic B\"uchi and co-B\"uchi automata, wDBAs have a minimal normal form, and we show that we can learn this minimal normal form efficiently. We provide an improved result on the number of queries required and show on benchmarks that this theoretical advantage translates into significantly fewer queries: while previous approaches require a quintic number of queries, we only require quadratically many queries in the size of the canonic wDBA that recognises the target language.