Developing a Comprehensive Foundation Model for Diverse Planning-like Tasks

Khái niệm cốt lõi

The core message of this paper is the need to develop a specialized Foundation Model (FM) for planning-like (PL) tasks, which goes beyond the capabilities of existing generic FMs. The authors argue that such a Planning FM, trained on diverse PL data and novel pre-training tasks, can open up new and efficient avenues for solving a wide range of PL problems.

Tóm tắt

The paper discusses the need for a comprehensive Foundation Model (FM) specifically designed for planning-like (PL) tasks, in contrast to the current approaches that primarily focus on adapting generic FMs for these tasks.
The key highlights and insights are:

PL tasks, such as business processes, design drawings, dialogs, guidelines, instructions, and workflows, involve generating a series of actions with varying execution guarantees. These tasks have unique requirements that are not fully captured by existing generic FMs.

Current approaches to FMs, including fine-tuning and prompting, have limitations in effectively modeling the nuances of PL tasks, such as understanding state, control flow, data flow, and execution semantics.

The authors propose the development of a Planning FM, which would be trained on a diverse corpus of PL data and novel pre-training tasks tailored to the specific needs of these tasks. This includes objectives like next-action prediction, conditional branching prediction, action-effect modeling, constraint satisfaction, and execution simulation.

The design of the Planning FM emphasizes compactness, generalizability, and an intrinsic awareness of temporal and execution considerations. This is achieved through techniques like model pruning, quantization, and knowledge distillation, as well as the incorporation of domain-specific ontologies and transfer learning.

The authors also discuss the importance of addressing the FM's properties of grounding, alignment, and instructability to ensure the generated plans are theoretically sound, practically executable, and adaptable to changing environments.

The proposed Planning FM is envisioned to excel at a variety of downstream PL tasks, including plan generation, plan completion, replanning, plan validity prediction, plan summarization, resource optimization, and error detection and correction.

Thống kê

None.

Trích dẫn

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Thông tin chi tiết chính được chắt lọc từ

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

by Biplav Sriva... lúc arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04540.pdf

Yêu cầu sâu hơn

How can the Planning FM leverage existing planning knowledge and techniques, such as classical planning formalisms and heuristics, to enhance its performance and generalizability?

Incorporating classical planning formalisms and heuristics into the training and design of the Planning FM can significantly enhance its performance and generalizability. By leveraging existing planning knowledge, the model can benefit from established principles and strategies that have proven effective in solving planning problems. Classical planning formalisms, such as Planning Domain Definition Language (PDDL), provide a structured way to represent planning tasks, including states, actions, and goals. By training the Planning FM on data encoded in PDDL or similar formalisms, the model can learn to understand and generate plans in a structured and systematic manner.
Additionally, integrating heuristics commonly used in classical planning, such as A* search or domain-specific heuristics, can guide the Planning FM in efficiently exploring the search space and finding optimal solutions. By incorporating these heuristics into the model's decision-making process, the Planning FM can improve its ability to generate high-quality plans within a reasonable time frame.
Furthermore, by drawing insights from classical planning techniques like plan validation, plan optimization, and plan execution semantics, the Planning FM can develop a deeper understanding of the intricacies involved in planning tasks. This knowledge can be instrumental in enhancing the model's performance across a wide range of planning-like tasks and improving its generalizability to new and unseen scenarios.

How can the potential challenges and limitations in training the Planning FM on a diverse corpus of PL data be addressed?

Training the Planning FM on a diverse corpus of planning-like (PL) data poses several challenges and limitations that need to be addressed to ensure the model's effectiveness and adaptability. Some potential challenges include data heterogeneity, data quality issues, domain-specific nuances, and scalability concerns. To address these challenges, the following strategies can be implemented:

Data Preprocessing and Augmentation: Prior to training, the PL data should undergo thorough preprocessing to standardize formats, handle missing values, and address inconsistencies. Data augmentation techniques can be employed to increase the diversity and size of the training dataset, ensuring the model learns from a wide range of scenarios.

Domain-Specific Embeddings: Utilizing domain-specific embeddings and ontologies can help the model capture the unique characteristics and relationships present in PL tasks. By incorporating domain knowledge into the training process, the Planning FM can better understand and generate plans that align with specific task requirements.

Transfer Learning and Fine-Tuning: Leveraging transfer learning techniques, where the model is pretrained on a large dataset and fine-tuned on PL data, can help mitigate the limitations of training on a limited corpus. Fine-tuning the model on task-specific data can enhance its performance on new tasks and improve generalizability.

Evaluation and Iterative Improvement: Continuous evaluation of the model's performance on diverse PL tasks is essential to identify shortcomings and areas for improvement. Iterative training cycles, incorporating feedback loops, can help refine the model's capabilities and address any data-related challenges that arise during training.

By implementing these strategies, the Planning FM can overcome potential challenges associated with training on diverse PL data and enhance its ability to generate high-quality plans across a wide range of scenarios.

Given the importance of grounding, alignment, and instructability for the Planning FM, how can these properties be effectively incorporated into the model's architecture and training process?

To effectively incorporate grounding, alignment, and instructability into the Planning FM's architecture and training process, the following strategies can be implemented:

Grounding:

Knowledge Graph Integration: Integrate a knowledge graph that captures domain-specific relationships and entities, enabling the model to ground its planning decisions in real-world knowledge.
Physical Constraints Verification: Implement mechanisms to verify physical constraints in plans, ensuring that the model's outputs are feasible and executable in practical scenarios.

Alignment:

Dynamic Goal Adjustment: Enable the model to dynamically adjust its planning strategies based on changes in objectives or environmental conditions, ensuring alignment with evolving goals.
Coherence Evaluation: Incorporate coherence evaluation mechanisms to assess the logical flow and consistency of generated plans, enhancing the model's alignment with predefined objectives.

Instructability:

Preference Learning: Integrate preference learning techniques to capture user preferences and constraints, allowing the model to generate plans that align with user-defined criteria.
Interactive Training: Implement interactive training sessions where users can provide feedback and corrections, enabling the model to learn from human instructions and improve its instructability over time.

By incorporating these properties into the model's architecture and training process, the Planning FM can enhance its ability to generate coherent, aligned, and instructable plans that meet the requirements of diverse planning-like tasks.