Large Language Models as Planning Domain Generators (Student Abstract)


  • James Oswald Rensselaer Polytechnic Institute
  • Kavitha Srinivas IBM Research
  • Harsha Kokel IBM Research
  • Junkyu Lee IBM Research
  • Michael Katz IBM Research
  • Shirin Sohrabi IBM Research



Automated Planning, Large Language Models, PDDL


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




How to Cite

Oswald, J., Srinivas, K., Kokel, H., Lee, J., Katz, M., & Sohrabi, S. (2024). Large Language Models as Planning Domain Generators (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23604-23605.