Large Language Models as Commonsense Knowledge for Large-Scale Task Planning

Zirui Zhao, Wee Sun Lee, David Hsu

Keywords: Embodied Task Planning, Large Language Models, Human-Robot Interaction

TL;DR: We use Large Language Models as both the commonsense world model and the heuristic policy within the Monte Carlo Tree Search framework, enabling better-reasoned decision-making for daily tasks.

Abstract:

Large-scale task planning is a major challenge. Recent work exploits large
language models (LLMs) directly as a policy and shows surprisingly
interesting results. This paper shows that LLMs provide a
commonsense model of the world in addition to a policy that acts on
it. The world model and the policy can be combined in a search
algorithm, such as Monte Carlo Tree Search (MCTS), to scale up task
planning. In our new LLM-MCTS algorithm, the LLM-induced world model
provides a commonsense prior belief for MCTS to achieve effective reasoning;
the LLM-induced policy acts as a heuristic to guide the search, vastly
improving search efficiency. Experiments show that LLM-MCTS outperforms
both MCTS alone and policies induced by LLMs (GPT2 and GPT3.5) by a wide
margin, for complex, novel tasks.
Further experiments and analyses on multiple tasks --
multiplication, travel planning, object rearrangement --
suggest minimum description length (MDL)
as a general guiding principle: if the
description length of the world model is substantially smaller than that of the
policy, using LLM as a world model for model-based planning is likely better
than using LLM solely as a policy.

Submission Number: 5591