Large Language Models as Planning Domain Generators
Authors: name: James Oswald, name: Kavitha Srinivas, name: Harsha Kokel, name: Junkyu Lee, name: Michael Katz, name: Shirin Sohrabi

Developing domain models is one of the few remaining places that require
manual human labor in AI planning. Thus, in order to make planning more
accessible, it is desirable to automate the process of domain model generation.
To this end, we investigate if large language models (LLMs) can be used to
generate planning domain models from simple textual descriptions. Specifically,
we introduce a framework for automated evaluation of LLM-generated domains 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, and under three classes of natural
language domain descriptions. Our results indicate that LLMs, particularly
those with high parameter counts, exhibit a moderate level of proficiency in
generating correct planning domains from natural language descriptions. Our
code is available at https://github.com/IBM/NL2PDDL.