There is a growing interest in applying pre-trained large language models
(LLMs) to planning problems. However, methods that use LLMs directly as
planners are currently impractical due to several factors, including limited
correctness of plans, strong reliance on feedback from interactions with
simulators or even the actual environment, and the inefficiency in utilizing
human feedback. In this work, we introduce a novel alternative paradigm that
constructs an explicit world (domain) model in planning domain definition
language (PDDL) and then uses it to plan with sound domain-independent
planners. To address the fact that LLMs may not generate a fully functional
PDDL model initially, we employ LLMs as an interface between PDDL and sources
of corrective feedback, such as PDDL validators and humans. For users who lack
a background in PDDL, we show that LLMs can translate PDDL into natural
language and effectively encode corrective feedback back to the underlying
domain model. Our framework not only enjoys the correctness guarantee offered
by the external planners but also reduces human involvement by allowing users
to correct domain models at the beginning, rather than inspecting and
correcting (through interactive prompting) every generated plan as in previous
work. On two IPC domains and a Household domain that is more complicated than
commonly used benchmarks such as ALFWorld, we demonstrate that GPT-4 can be
leveraged to produce high-quality PDDL models for over 40 actions, and the
corrected PDDL models are then used to successfully solve 48 challenging
planning tasks. Resources including the source code will be released at:
this https URL

本研究引入了一种新的方法，使用 PDDL 语言构建显式世界模型，并利用预训练的大型语言模型作为 PDDL 和校验器等纠正反馈的接口，以提高计划问题的效率和准确性。在不涉及用户互动的情况下，通过验证 PDDL 模型的正确性，我们制定计划来解决复杂任务并取得成功。

利用预训练的大型语言模型构建和利用世界模型进行基于模型的任务规划

Leveraging Pre-trained Large Language Models to Construct and Utilize  World Models for Model-based Task Planning

The problem of integrating high-level task planning in the execution loop of
a real-world robot architecture remains challenging, as the planning times of
traditional symbolic planners explode combinatorially with the number of
symbols to plan upon. In this paper, we present Teriyaki, a framework for
training Large Language Models (LLMs), and in particular the now well-known
GPT-3 model, into neurosymbolic planners compatible with the Planning Domain
Definition Language (PDDL). Unlike symbolic approaches, LLMs require a training
process. However, their response time scales with the combined length of the
input and the output. Hence, LLM-based planners can potentially provide
significant performance gains on complex planning problems as the technology
matures and becomes more accessible. In this preliminary work, which to our
knowledge is the first using LLMs for planning in robotics, we (i) outline a
methodology for training LLMs as PDDL solvers, (ii) generate PDDL-compliant
planners for two challenging PDDL domains, and (iii) test the planning times
and the plan quality associated with the obtained planners, while also
comparing them to a state-of-the-art PDDL planner, namely Probe. Results
confirm the viability of the approach, with Teriyaki-based planners being able
to solve 95.5% of problems in a test data set of 1000 samples, and even
generating plans up to 13.5% shorter on average than the employed traditional
planner, depending on the domain.

本文介绍了 Teriyaki 框架，将大型语言模型（LLMs）训练为与规划领域定义语言（PDDL）兼容的神经符号规划器，验证了这种基于 LLMs 的规划器的正确性，并将其与现有的 PDDL 规划器进行了比较。

生成符合 PDDL 标准的神经符号规划器框架

A Framework to Generate Neurosymbolic PDDL-compliant Planners

We achieved a new milestone in the difficult task of enabling agents to learn
about their environment autonomously. Our neuro-symbolic architecture is
trained end-to-end to produce a succinct and effective discrete state
transition model from images alone. Our target representation (the Planning
Domain Definition Language) is already in a form that off-the-shelf solvers can
consume, and opens the door to the rich array of modern heuristic search
capabilities. We demonstrate how the sophisticated innate prior we place on the
learning process significantly reduces the complexity of the learned
representation, and reveals a connection to the graph-theoretic notion of
"cube-like graphs", thus opening the door to a deeper understanding of the
ideal properties for learned symbolic representations. We show that the
powerful domain-independent heuristics allow our system to solve visual
15-Puzzle instances which are beyond the reach of blind search, without
resorting to the Reinforcement Learning approach that requires a huge amount of
training on the domain-dependent reward information.

我们提出了一种新型的神经符号结构，通过从图像中学习出稳固而高效的离散状态转移模型，兼容基于启发式搜索的现代解算器，并且通过在学习过程中加入有利的先验知识降低了学习精度，并开展其对于学习符号表现的优良特质的理论探索。