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arXiv · 2206.09619

Analyzing B\"uchi Automata with Graph Neural Networks

Christophe Stammet·Prisca Dotti·Ulrich Ultes-Nitsche·Andreas Fischer
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Abstract

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.

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Christophe Stammet, Prisca Dotti, Ulrich Ultes-Nitsche, Andreas Fischer. 2022-06-20. Analyzing B\"uchi Automata with Graph Neural Networks. https://arxiv.org/abs/2206.09619

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