Analyzing B\'uchi Automata with Graph Neural Networks

Autor:	Stammet, Christophe, Dotti, Prisca, Ultes-Nitsche, Ulrich, Fischer, Andreas
Rok vydání:	2022
Předmět:	Computer Science - Formal Languages and Automata Theory Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	B\"uchi Automata on infinite words present many interesting problems and are used frequently in program verification and model checking. A lot of these problems on B\"uchi automata are computationally hard, raising the question if a learning-based data-driven analysis might be more efficient than using traditional algorithms. Since B\"uchi automata can be represented by graphs, graph neural networks are a natural choice for such a learning-based analysis. In this paper, we demonstrate how graph neural networks can be used to reliably predict basic properties of B\"uchi automata when trained on automatically generated random automata datasets. Comment: Accepted for presentation at Workshop LearnAut 2022 (https://learnaut22.github.io/index.html)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2206.09619 Zobrazit plný text záznamu View this record from Arxiv