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。