ASMR extracts concepts with an LLM, clusters them into candidate fields, then uses RL to select compact non-redundant schemas for each ship-report form type.
Large language models as planning domain generators
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
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.
fields
cs.AI 2years
2026 2representative citing papers
A staged LLM pipeline synthesizes verifiable discrete-event world models from natural language specifications using the DEVS formalism for long-horizon consistency in LLM agents.
citing papers explorer
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ASMR: Agentic Schema Generation for Ship Maintenance Report Writing
ASMR extracts concepts with an LLM, clusters them into candidate fields, then uses RL to select compact non-redundant schemas for each ship-report form type.
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Specification-Driven Generation and Evaluation of Discrete-Event World Models via the DEVS Formalism
A staged LLM pipeline synthesizes verifiable discrete-event world models from natural language specifications using the DEVS formalism for long-horizon consistency in LLM agents.