On the Roles of LLMs in Planning: Embedding LLMs into Planning Graphs
arxiv(2024)
摘要
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.
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