On the Roles of LLMs in Planning: Embedding LLMs into Planning Graphs
This work addresses the challenge of leveraging LLMs for planning tasks in AI, offering an incremental improvement by embedding them into off-the-shelf planning graphs.
The paper tackles the problem of integrating large language models (LLMs) into existing planning frameworks to enhance plan synthesis, proposing a novel method that embeds LLMs into graph-based planning at two levels and demonstrating its effectiveness across various domains.
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.