Plan synthesis aims to generate a course of actions or policies to transit
given initial states to goal states, provided domain models that could be
designed by experts or learnt from training data or interactions with the
world. Intrigued by the claims of emergent planning capabilities in large
language models (LLMs), works have been proposed to investigate the planning
effectiveness of LLMs, without considering any utilization of off-the-shelf
planning techniques in LLMs. In this paper, we aim to further study the insight
of the planning capability of LLMs by investigating the roles of LLMs in
off-the-shelf planning frameworks. To do this, we investigate the effectiveness
of embedding LLMs into one of the well-known planning frameworks, graph-based
planning, proposing a novel LLMs-based planning framework with LLMs embedded in
two levels of planning graphs, i.e., mutual constraints generation level and
constraints solving level. We empirically exhibit the effectiveness of our
proposed framework in various planning domains.

通过对大型语言模型（LLMs）嵌入已知的规划框架中的研究，我们展示了在各种规划领域中，这种新型基于 LLMs 的规划框架的有效性。

规划中 LLMs 的作用：将 LLMs 嵌入到规划图中

On the Roles of LLMs in Planning: Embedding LLMs into Planning Graphs

Generalized planning aims at computing an algorithm-like structure
(generalized plan) that solves a set of multiple planning instances. In this
paper we define negative examples for generalized planning as planning
instances that must not be solved by a generalized plan. With this regard the
paper extends the notion of validation of a generalized plan as the problem of
verifying that a given generalized plan solves the set of input positives
instances while it fails to solve a given input set of negative examples. This
notion of plan validation allows us to define quantitative metrics to asses the
generalization capacity of generalized plans. The paper also shows how to
incorporate this new notion of plan validation into a compilation for plan
synthesis that takes both positive and negative instances as input. Experiments
show that incorporating negative examples can accelerate plan synthesis in
several domains and leverage quantitative metrics to evaluate the
generalization capacity of the synthesized plans.

本文将负例规划定义为一组不应由泛化计划解决的计划实例，并将计划验证的概念扩展到验证给定泛化计划解决输入正例实例时是否无法解决给定的一组负例实例，以量化评估泛化计划的能力，并展示如何将负例规划应用于计划综合的编译中，实验结果表明添加负例可以加速计划综合的过程，并提高合成计划泛化能力的定量度量方式。