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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.11814 |
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| _version_ | 1866916741975638016 |
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| author | Munoz-Avila, Hector Aha, David W. Rizzo, Paola |
| author_facet | Munoz-Avila, Hector Aha, David W. Rizzo, Paola |
| contents | We introduce ChatHTN, a Hierarchical Task Network (HTN) planner that combines symbolic HTN planning techniques with queries to ChatGPT to approximate solutions in the form of task decompositions. The resulting hierarchies interleave task decompositions generated by symbolic HTN planning with those generated by ChatGPT. Despite the approximate nature of the results generates by ChatGPT, ChatHTN is provably sound; any plan it generates correctly achieves the input tasks. We demonstrate this property with an open-source implementation of our system. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_11814 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ChatHTN: Interleaving Approximate (LLM) and Symbolic HTN Planning Munoz-Avila, Hector Aha, David W. Rizzo, Paola Artificial Intelligence We introduce ChatHTN, a Hierarchical Task Network (HTN) planner that combines symbolic HTN planning techniques with queries to ChatGPT to approximate solutions in the form of task decompositions. The resulting hierarchies interleave task decompositions generated by symbolic HTN planning with those generated by ChatGPT. Despite the approximate nature of the results generates by ChatGPT, ChatHTN is provably sound; any plan it generates correctly achieves the input tasks. We demonstrate this property with an open-source implementation of our system. |
| title | ChatHTN: Interleaving Approximate (LLM) and Symbolic HTN Planning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2505.11814 |