Large Language Models as Planning Domain Generators

• URL: http://arxiv.org/abs/2405.06650v1
• Date: Tue, 2 Apr 2024 19:39:23 GMT
• Title: Large Language Models as Planning Domain Generators
• Authors: James Oswald, Kavitha Srinivas, Harsha Kokel, Junkyu Lee, Michael Katz, Shirin Sohrabi,
• Abstract summary: Large language models (LLMs) can be used to generate planning domain models from simple textual descriptions. LLMs exhibit a moderate level of proficiency in generating correct planning domains from natural language descriptions.
• Score: 21.61328475841776
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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