Output-decomposed Learning of Mealy Machines
<|reference_start|>Output-decomposed Learning of Mealy Machines: 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.<|reference_end|>
@article{koenders2024output-decomposed,
    title={Output-decomposed Learning of Mealy Machines},
    author={Rick Koenders and Joshua Moerman},
    journal={arXiv preprint arXiv:2405.08647},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.08647},
    primaryClass={cs.LO cs.LG}
}