# Output-decomposed Learning of Mealy Machines

arXiv: https://arxiv.org/abs/2405.08647 · HTML: https://mlsanity.com/paper/2405.08647/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 13 references that are in the map.
- First posted on arXiv 14 May 2024.

## Builds on (most cited first, 1 of 1)

- [Learning Product Automata](https://mlsanity.com/paper/1705.02850/) (2017)

## Abstract (arXiv)

We present an active automata learning algorithm which learns a decomposition of a finite state machine, based on projecting onto individual outputs. This is dual to a recent compositional learning algorithm by Labbaf et al. (2023). When projecting the outputs to a smaller set, the model itself is reduced in size. By having several such projections, we do not lose any information and the full system can be reconstructed. Depending on the structure of the system this reduces the number of queries drastically, as shown by a preliminary evaluation of the algorithm.