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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Online-Zugang: | https://arxiv.org/abs/2409.12300 |
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| _version_ | 1866916434592923648 |
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| author | Mensfelt, Agnieszka Stathis, Kostas Trencsenyi, Vince |
| author_facet | Mensfelt, Agnieszka Stathis, Kostas Trencsenyi, Vince |
| contents | Game theory is a powerful framework for reasoning about strategic interactions, with applications in domains ranging from day-to-day life to international politics. However, applying formal reasoning tools in such contexts is challenging, as these scenarios are often expressed in natural language. To address this, we introduce a framework for the autoformalization of game-theoretic scenarios, which translates natural language descriptions into formal logic representations suitable for formal solvers. Our approach utilizes one-shot prompting and a solver that provides feedback on syntactic correctness to allow LLMs to refine the code. We evaluate the framework using GPT-4o and a dataset of natural language problem descriptions, achieving 98% syntactic correctness and 88% semantic correctness. These results show the potential of LLMs to bridge the gap between real-life strategic interactions and formal reasoning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_12300 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Autoformalization of Game Descriptions using Large Language Models Mensfelt, Agnieszka Stathis, Kostas Trencsenyi, Vince Artificial Intelligence Computer Science and Game Theory Game theory is a powerful framework for reasoning about strategic interactions, with applications in domains ranging from day-to-day life to international politics. However, applying formal reasoning tools in such contexts is challenging, as these scenarios are often expressed in natural language. To address this, we introduce a framework for the autoformalization of game-theoretic scenarios, which translates natural language descriptions into formal logic representations suitable for formal solvers. Our approach utilizes one-shot prompting and a solver that provides feedback on syntactic correctness to allow LLMs to refine the code. We evaluate the framework using GPT-4o and a dataset of natural language problem descriptions, achieving 98% syntactic correctness and 88% semantic correctness. These results show the potential of LLMs to bridge the gap between real-life strategic interactions and formal reasoning. |
| title | Autoformalization of Game Descriptions using Large Language Models |
| topic | Artificial Intelligence Computer Science and Game Theory |
| url | https://arxiv.org/abs/2409.12300 |