Official repository for the paper "DeepDFA: Automata Learning through Neural Probabilistic Relaxations" accepted by the 27th European Conference on Artificial Intelligence (ECAI 2024).
- Pythomata
- Pytorch
To reproduce the experiments in the paper execute the file do_experiments.py
.
Run do_experiments.py --help
for a complete list of the parameters used by the script (below).
USAGE: do_experiments.py [flags]
flags:
do_experiments.py:
--dataset_dir: path to the datasets
(default: 'datasets_Tomita/')
--error_rate: portion of errated labels in the training dataset
(default: '0.0')
(a number)
--experiment: which experiment to execute, choose one in the list ['tomita', 'random_dfa']
(default: 'tomita')
--ground_truth_dfa_dir: path to save ground truth automatons
(default: 'DFA_ground_truth/')
--plot_dir: path to save the plots
(default: 'Plots/')
--predicted_dfa_dir: path to save predicted automatons
(default: 'DFA_predicted_nesy/')
--results_dir: path to save the results
(default: 'Results/')
Examples:
To learn the Tomita languages from data execute the script with default parameters
python do_experiments.py
To learn the random DFAs in the directory random_DFAs
run
python do_experiments.py --dataset_dir datasets_random_dfa/ --experiment random_dfa
To repeat the expermints on a corrupted version of the datasets, with 1% of flipped labels, add to the commands above --error_rate 0.01
@inproceedings{UmiliDeepDFA,
author = {Elena Umili and
Roberto Capobianco},
editor = {Ulle Endriss and
Francisco S. Melo and
Kerstin Bach and
Alberto Jos{\'{e}} Bugar{\'{\i}}n Diz and
Jose Maria Alonso{-}Moral and
Sen{\'{e}}n Barro and
Fredrik Heintz},
title = {DeepDFA: Automata Learning through Neural Probabilistic Relaxations},
booktitle = {{ECAI} 2024 - 27th European Conference on Artificial Intelligence,
19-24 October 2024, Santiago de Compostela, Spain - Including 13th
Conference on Prestigious Applications of Intelligent Systems {(PAIS}
2024)},
series = {Frontiers in Artificial Intelligence and Applications},
volume = {392},
pages = {1051--1058},
publisher = {{IOS} Press},
year = {2024},
url = {https://doi.org/10.3233/FAIA240596},
doi = {10.3233/FAIA240596},
timestamp = {Fri, 25 Oct 2024 12:13:46 +0200},
biburl = {https://dblp.org/rec/conf/ecai/UmiliC24.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}