Large Language Models as Planning Domain Generators (Student Abstract)
Abstract
The creation of planning models, and in particular domain models, is among the last bastions of tasks that require exten- sive manual labor in AI planning; it is desirable to simplify this process for the sake of making planning more accessi- ble. To this end, we investigate whether large language mod- els (LLMs) can be used to generate planning domain models from textual descriptions. We propose a novel task for this as well as a means of automated evaluation for generated do- mains by comparing the sets of plans for domain instances. Finally, we perform an empirical analysis of 7 large language models, including coding and chat models across 9 different planning domains. Our results show that LLMs, particularly larger ones, exhibit some level of proficiency in generating correct planning domains from natural language descriptions
Cite
Text
Oswald et al. "Large Language Models as Planning Domain Generators (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I21.30491Markdown
[Oswald et al. "Large Language Models as Planning Domain Generators (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/oswald2024aaai-large/) doi:10.1609/AAAI.V38I21.30491BibTeX
@inproceedings{oswald2024aaai-large,
title = {{Large Language Models as Planning Domain Generators (Student Abstract)}},
author = {Oswald, James T. and Srinivas, Kavitha and Kokel, Harsha and Lee, Junkyu and Katz, Michael and Sohrabi, Shirin},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2024},
pages = {23604-23605},
doi = {10.1609/AAAI.V38I21.30491},
url = {https://mlanthology.org/aaai/2024/oswald2024aaai-large/}
}