Foundation Models (FMs) have revolutionized many areas of computing,
including Automated Planning and Scheduling (APS). For example, a recent study
found them useful for planning problems: plan generation, language translation,
model construction, multi-agent planning, interactive planning, heuristics
optimization, tool integration, and brain-inspired planning. Besides APS, there
are many seemingly related tasks involving the generation of a series of
actions with varying guarantees of their executability to achieve intended
goals, which we collectively call planning-like (PL) tasks like business
processes, programs, workflows, and guidelines, where researchers have
considered using FMs. However, previous works have primarily focused on
pre-trained, off-the-shelf FMs and optionally fine-tuned them. This paper
discusses the need for a comprehensive FM for PL tasks from scratch and
explores its design considerations. We argue that such an FM will open new and
efficient avenues for PL problem-solving, just like LLMs are creating for APS.

基于先前使用预训练的基础模型和微调选项的工作，本研究讨论了从零开始创建全面的基础模型以解决规划类任务的需求，并探讨了其设计考虑因素。我们认为这样的基础模型将为问题解决提供新的高效途径，就像 LLMs 正在为 APS 领域创造的一样。

从零开始发展规划类任务的基础模型的案例

The Case for Developing a Foundation Model for Planning-like Tasks from  Scratch

Automated Planning and Scheduling is among the growing areas in Artificial
Intelligence (AI) where mention of LLMs has gained popularity. Based on a
comprehensive review of 126 papers, this paper investigates eight categories
based on the unique applications of LLMs in addressing various aspects of
planning problems: language translation, plan generation, model construction,
multi-agent planning, interactive planning, heuristics optimization, tool
integration, and brain-inspired planning. For each category, we articulate the
issues considered and existing gaps. A critical insight resulting from our
review is that the true potential of LLMs unfolds when they are integrated with
traditional symbolic planners, pointing towards a promising neuro-symbolic
approach. This approach effectively combines the generative aspects of LLMs
with the precision of classical planning methods. By synthesizing insights from
existing literature, we underline the potential of this integration to address
complex planning challenges. Our goal is to encourage the ICAPS community to
recognize the complementary strengths of LLMs and symbolic planners, advocating
for a direction in automated planning that leverages these synergistic
capabilities to develop more advanced and intelligent planning systems.

自主规划和调度是人工智能中增长最快的领域之一，本研究通过对 126 篇论文进行全面检视，调查了 LLMs 在地址规划问题的各个方面时的独特应用，并提出了八个类别：语言翻译、计划生成、模型构建、多智能体规划、交互规划、启发式优化、工具集成以及脑启发式规划，强调 LLMs 与传统符号规划器的结合展示出潜力，揭示出一种有前景的神经符号化方法。