Summary of Leveraging Environment Interaction For Automated Pddl Translation and Planning with Large Language Models, by Sadegh Mahdavi et al.
Leveraging Environment Interaction for Automated PDDL Translation and Planning with Large Language Models
by Sadegh Mahdavi, Raquel Aoki, Keyi Tang, Yanshuai Cao
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The paper proposes a novel approach to automatically generating Planning Domain Definition Language (PDDL) files without human intervention, leveraging Large Language Models (LLMs) and environment feedback. The method introduces an iterative refinement process that generates multiple problem PDDL candidates and refines the domain PDDL based on feedback from interacting with the environment. An Exploration Walk (EW) metric guides the refinement process. Evaluation on 10 PDDL environments shows an average task solve rate of 66%, outperforming GPT-4’s intrinsic planning with chain-of-thought prompting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps LLMs plan better by automatically creating PDDL files, which are used to make decisions. The approach uses a special metric called Exploration Walk (EW) to improve the planning process. The result is more accurate and efficient decision-making. |
Keywords
» Artificial intelligence » Gpt » Prompting
