The Case for Developing a Foundation Model for Planning-like Tasks from Scratch
This work addresses the need for specialized foundation models in planning-like domains, proposing a foundational shift rather than incremental improvements.
The paper argues for developing a new foundation model from scratch specifically for planning-like tasks, such as business processes and workflows, to enable more efficient problem-solving, similar to how large language models have impacted automated planning.
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.