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The Case for Developing a Foundation Model for Planning-like Tasks from Scratch (2404.04540v1)

Published 6 Apr 2024 in cs.AI

Abstract: 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.

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Citations (2)
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Summary

  • The paper establishes that current NLP foundation models lack the formalism required for generating precise action sequences in planning-like tasks.
  • It identifies a critical gap in handling execution semantics and task-specific complexities that general models fail to capture.
  • It proposes a novel training methodology incorporating custom tokenizers, pre-training tasks, and evaluation metrics to create efficient, domain-tailored models.

Overview of the Need for a Foundation Model for Planning-like Tasks

The paper "The Case for Developing a Foundation Model for Planning-like Tasks from Scratch" by Biplav Srivastava and Vishal Pallagani presents an intriguing discussion on the need to develop a specialized Foundation Model (FM) to address Planning-like (PL) tasks. The authors argue that existing Foundation Models, primarily pre-trained for NLP tasks, are inadequate for capturing the nuanced requirements of planning tasks that involve generating specific action sequences with varied execution guarantees. The paper outlines a framework for designing and training a bespoke FM specifically tailored to meet the intricate demands of PL tasks, leveraging insights from Automated Planning and Scheduling (APS).

Key Contributions

  1. Clarification of Planning-like Tasks: The paper introduces the concept of PL tasks, encompassing business processes, dialogues, guidelines, instructions, design drawings, programs, and workflows. Each of these tasks involves generating sequences of actions or decisions to achieve specific goals. While existing FMs are being explored for these tasks, their efficacy remains limited due to the lack of proper formalism akin to that available in APS.
  2. Identifying the Gap: Current FMs are predicated on general pre-training tasks and datasets, making them ill-equipped to handle intricacies crucial to PL tasks, such as action sequence generation, execution semantics, and task-specific complexities. The paper highlights the limited success of fine-tuning off-the-shelf models for domain-specific applications and suggests that starting from scratch could result in models better aligned with PL objectives.
  3. Proposed Training Methodology: A comprehensive training procedure is proposed that involves developing a specialized tokenizer, model architecture, and novel pre-training tasks aimed at capturing PL tasks' complex requirements. The paper also suggests leveraging domain-specific datasets and proposed evaluation metrics tailored to assess the FM's performance on PL tasks.
  4. Novel Pre-training Tasks: Drawing attention to limitations in current FMs, the paper suggests unique pre-training tasks such as Next Action Prediction, Execution Simulation, and Action and Effect Modeling. These tasks aim to impart an understanding of temporal planning, execution semantics, and action consequences, thereby enhancing the model's decision-making prowess.
  5. Implications and Practical Considerations: Beyond theoretical implications, the paper discusses practical considerations in developing such an FM, including strategies for pruning, quantization, and knowledge distillation to develop compact and efficient models that can be deployed in resource-constrained environments.

Implications for Future Research

The pursuit of a Foundation Model customized for PL tasks could significantly impact both theoretical approaches to AI planning and practical implementations across domains such as business process management, software engineering, and complex task orchestration. By incorporating multi-modal learning and domain-specific pre-training paradigms, future research could explore cross-domain applicability and the challenges related to grounding, alignment, and instructability, which are crucial for effective real-world deployment.

Conclusion

The paper presents a persuasive argument for the necessity of developing a Foundation Model specifically designed for Planning-like tasks. It addresses the inadequacies of existing models for this purpose and offers a detailed roadmap for creating more specialized, effective, and efficient systems tailored to the diverse needs of PL tasks. This work establishes the foundational considerations essential for advancing AI's capability in generating, executing, and validating plans across a broad spectrum of applications.

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