NeuroSynt: A Neuro-symbolic Portfolio Solver for Reactive Synthesis
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929227002019840 |
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| author | Cosler, Matthias Hahn, Christopher Omar, Ayham Schmitt, Frederik |
| author_facet | Cosler, Matthias Hahn, Christopher Omar, Ayham Schmitt, Frederik |
| contents | We introduce NeuroSynt, a neuro-symbolic portfolio solver framework for reactive synthesis. At the core of the solver lies a seamless integration of neural and symbolic approaches to solving the reactive synthesis problem. To ensure soundness, the neural engine is coupled with model checkers verifying the predictions of the underlying neural models. The open-source implementation of NeuroSynt provides an integration framework for reactive synthesis in which new neural and state-of-the-art symbolic approaches can be seamlessly integrated. Extensive experiments demonstrate its efficacy in handling challenging specifications, enhancing the state-of-the-art reactive synthesis solvers, with NeuroSynt contributing novel solves in the current SYNTCOMP benchmarks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_12131 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | NeuroSynt: A Neuro-symbolic Portfolio Solver for Reactive Synthesis Cosler, Matthias Hahn, Christopher Omar, Ayham Schmitt, Frederik Logic in Computer Science Machine Learning We introduce NeuroSynt, a neuro-symbolic portfolio solver framework for reactive synthesis. At the core of the solver lies a seamless integration of neural and symbolic approaches to solving the reactive synthesis problem. To ensure soundness, the neural engine is coupled with model checkers verifying the predictions of the underlying neural models. The open-source implementation of NeuroSynt provides an integration framework for reactive synthesis in which new neural and state-of-the-art symbolic approaches can be seamlessly integrated. Extensive experiments demonstrate its efficacy in handling challenging specifications, enhancing the state-of-the-art reactive synthesis solvers, with NeuroSynt contributing novel solves in the current SYNTCOMP benchmarks. |
| title | NeuroSynt: A Neuro-symbolic Portfolio Solver for Reactive Synthesis |
| topic | Logic in Computer Science Machine Learning |
| url | https://arxiv.org/abs/2401.12131 |