Guiding Word Equation Solving using Graph Neural Networks (Extended Technical Report)
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917847347757056 |
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| author | Abdulla, Parosh Aziz Atig, Mohamed Faouzi Cailler, Julie Liang, Chencheng Rümmer, Philipp |
| author_facet | Abdulla, Parosh Aziz Atig, Mohamed Faouzi Cailler, Julie Liang, Chencheng Rümmer, Philipp |
| contents | This paper proposes a Graph Neural Network-guided algorithm for solving word equations, based on the well-known Nielsen transformation for splitting equations. The algorithm iteratively rewrites the first terms of each side of an equation, giving rise to a tree-like search space. The choice of path at each split point of the tree significantly impacts solving time, motivating the use of Graph Neural Networks (GNNs) for efficient split decision-making. Split decisions are encoded as multi-classification tasks, and five graph representations of word equations are introduced to encode their structural information for GNNs. The algorithm is implemented as a solver named DragonLi. Experiments are conducted on artificial and real-world benchmarks. The algorithm performs particularly well on satisfiable problems. For single word \mbox{equations}, DragonLi can solve significantly more problems than well-established string solvers. For the conjunction of multiple word equations, DragonLi is competitive with state-of-the-art string solvers. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_15194 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Guiding Word Equation Solving using Graph Neural Networks (Extended Technical Report) Abdulla, Parosh Aziz Atig, Mohamed Faouzi Cailler, Julie Liang, Chencheng Rümmer, Philipp Machine Learning Artificial Intelligence Computation and Language Logic in Computer Science This paper proposes a Graph Neural Network-guided algorithm for solving word equations, based on the well-known Nielsen transformation for splitting equations. The algorithm iteratively rewrites the first terms of each side of an equation, giving rise to a tree-like search space. The choice of path at each split point of the tree significantly impacts solving time, motivating the use of Graph Neural Networks (GNNs) for efficient split decision-making. Split decisions are encoded as multi-classification tasks, and five graph representations of word equations are introduced to encode their structural information for GNNs. The algorithm is implemented as a solver named DragonLi. Experiments are conducted on artificial and real-world benchmarks. The algorithm performs particularly well on satisfiable problems. For single word \mbox{equations}, DragonLi can solve significantly more problems than well-established string solvers. For the conjunction of multiple word equations, DragonLi is competitive with state-of-the-art string solvers. |
| title | Guiding Word Equation Solving using Graph Neural Networks (Extended Technical Report) |
| topic | Machine Learning Artificial Intelligence Computation and Language Logic in Computer Science |
| url | https://arxiv.org/abs/2411.15194 |