This is the first work to look at the application of large language models
(LLMs) for the purpose of model space edits in automated planning tasks. To set
the stage for this sangam, we explore two different flavors of model space
problems that have been studied in the AI planning literature and explore the
effect of an LLM on those tasks. We empirically demonstrate how the performance
of an LLM contrasts with combinatorial search (CS) - an approach that has been
traditionally used to solve model space tasks in planning, both with the LLM in
the role of a standalone model space reasoner as well as in the role of a
statistical signal in concert with the CS approach as part of a two-stage
process. Our experiments show promising results suggesting further forays of
LLMs into the exciting world of model space reasoning for planning tasks in the
future.

这是第一项研究应用大语言模型（LLMs）进行自动规划任务模型空间编辑的工作。我们探索了 AI 规划文献中研究的两种不同类型的模型空间问题，以及 LLM 对这些任务的影响。我们通过实验证明了 LLM 的性能与组合搜索（CS）的对比情况，以及 LLM 作为独立模型空间推理器以及作为与 CS 方法结合的统计信号的双阶段过程的一部分时的性能。我们的实验结果表明，LLM 在未来进一步探索规划任务中的模型空间推理的前景很有希望。