Summary of On the Prospects Of Incorporating Large Language Models (llms) in Automated Planning and Scheduling (aps), by Vishal Pallagani et al.
On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS)
by Vishal Pallagani, Kaushik Roy, Bharath Muppasani, Francesco Fabiano, Andrea Loreggia, Keerthiram Murugesan, Biplav Srivastava, Francesca Rossi, Lior Horesh, Amit Sheth
First submitted to arxiv on: 4 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Automated Planning and Scheduling, an emerging area within Artificial Intelligence (AI), is gaining traction with the mention of Large Language Models (LLMs). This study reviews 126 papers across eight categories, including language translation, plan generation, model construction, multi-agent planning, interactive planning, heuristics optimization, tool integration, and brain-inspired planning. The review highlights issues considered and existing gaps in each category. A key finding is that LLMs’ true potential is unleashed when integrated with traditional symbolic planners, enabling a neuro-symbolic approach. This combination leverages the generative capabilities of LLMs and the precision of classical planning methods. By synthesizing insights from existing literature, this study underscores the potential of this integration to address complex planning challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Automated Planning and Scheduling is an exciting area in Artificial Intelligence that’s getting more attention. Researchers are exploring how Large Language Models (LLMs) can help with planning and scheduling tasks. This paper looked at 126 studies across different areas, like translating plans into other languages or using AI to optimize schedules. The study found that LLMs work best when used together with traditional planners, which helps make them more accurate and efficient. By combining the strengths of these approaches, researchers can develop more advanced planning systems. |
Keywords
» Artificial intelligence » Attention » Optimization » Precision » Translation
