Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.08068 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911281407066112 |
|---|---|
| author | Puerta-Merino, Israel Núñez-Molina, Carlos Mesejo, Pablo Fernández-Olivares, Juan |
| author_facet | Puerta-Merino, Israel Núñez-Molina, Carlos Mesejo, Pablo Fernández-Olivares, Juan |
| contents | Recent advances in Large Language Models (LLMs) are fostering their integration into several reasoning-related fields, including Automated Planning (AP). However, their integration into Hierarchical Planning (HP), a subfield of AP that leverages hierarchical knowledge to enhance planning performance, remains largely unexplored. In this preliminary work, we propose a roadmap to address this gap and harness the potential of LLMs for HP. To this end, we present a taxonomy of integration methods, exploring how LLMs can be utilized within the HP life cycle. Additionally, we provide a benchmark with a standardized dataset for evaluating the performance of future LLM-based HP approaches, and present initial results for a state-of-the-art HP planner and LLM planner. As expected, the latter exhibits limited performance (3\% correct plans, and none with a correct hierarchical decomposition) but serves as a valuable baseline for future approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_08068 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Roadmap to Guide the Integration of LLMs in Hierarchical Planning Puerta-Merino, Israel Núñez-Molina, Carlos Mesejo, Pablo Fernández-Olivares, Juan Artificial Intelligence Recent advances in Large Language Models (LLMs) are fostering their integration into several reasoning-related fields, including Automated Planning (AP). However, their integration into Hierarchical Planning (HP), a subfield of AP that leverages hierarchical knowledge to enhance planning performance, remains largely unexplored. In this preliminary work, we propose a roadmap to address this gap and harness the potential of LLMs for HP. To this end, we present a taxonomy of integration methods, exploring how LLMs can be utilized within the HP life cycle. Additionally, we provide a benchmark with a standardized dataset for evaluating the performance of future LLM-based HP approaches, and present initial results for a state-of-the-art HP planner and LLM planner. As expected, the latter exhibits limited performance (3\% correct plans, and none with a correct hierarchical decomposition) but serves as a valuable baseline for future approaches. |
| title | A Roadmap to Guide the Integration of LLMs in Hierarchical Planning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2501.08068 |