# Learning Product Automata

arXiv: https://arxiv.org/abs/1705.02850 · HTML: https://mlsanity.com/paper/1705.02850/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2017.

## Built on by (most cited first, 3 of 3)

- [Extracting Moore Machines from Transformers using Queries and Counterexamples](https://mlsanity.com/paper/2410.06045/) (2024)
- [Automata Cascades: Expressivity and Sample Complexity](https://mlsanity.com/paper/2211.14028/) (2022)
- [Output-decomposed Learning of Mealy Machines](https://mlsanity.com/paper/2405.08647/) (2024)

## Abstract (arXiv)

In this paper we give an optimization for active learning algorithms, applicable to learning Moore machines where the output comprises several observables. These machines can be decomposed themselves by projecting on each observable, resulting in smaller components. These components can then be learnt with fewer queries. This is in particular interesting for learning software, where compositional methods are important for guaranteeing scalability.