Summary of Pddlfuse: a Tool For Generating Diverse Planning Domains, by Vedant Khandelwal et al.
PDDLFuse: A Tool for Generating Diverse Planning Domains
by Vedant Khandelwal, Amit Sheth, Forest Agostinelli
First submitted to arxiv on: 29 Nov 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 The paper proposes a novel approach to generating new planning domains using generative AI technologies like large language models (LLMs). This is in contrast to existing methods that rely on human implementation, which limits the scale and diversity of available domains. The tool, PDDLFuse, aims to create new, diverse planning domains that can be used to validate new planners or test foundational planning models. It uses domain randomization, a technique that has been effective in reinforcement learning, to enhance performance and generalizability by training on a diverse array of randomized new domains. The paper also introduces methods to adjust the domain generators’ parameters to modulate the difficulty of the domains it generates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps with making planning algorithms more adaptable and better able to solve real-world problems. It uses special computer programs called large language models to create new planning “worlds” that can be used to test how well different planning methods work. This is important because planning algorithms need to be able to handle many different kinds of problems, not just the ones we already know about. The paper also shows that its approach can make the planning domains it creates more challenging and interesting. |
Keywords
» Artificial intelligence » Reinforcement learning
