Summary of Graph Learning For Planning: the Story Thus Far and Open Challenges, by Dillon Z. Chen et al.
Graph Learning for Planning: The Story Thus Far and Open Challenges
by Dillon Z. Chen, Mingyu Hao, Sylvie Thiébaux, Felipe Trevizan
First submitted to arxiv on: 3 Dec 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 In this paper, researchers explore the application of graph learning in planning domains, leveraging its ability to utilize relational structures. They investigate the effects of different graph representations, learning architectures, and optimization formulations on learning and planning performance. The GOOSE framework is introduced, which learns domain knowledge from small tasks to scale up to larger ones. This study also identifies five open challenges in the Learning for Planning field that need to be addressed to advance the state-of-the-art. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Planning involves making decisions based on relational structures. Graph learning can help with this by understanding these relationships and using them to make better decisions. The researchers studied how different graph representations, learning methods, and optimization techniques affect planning performance. They also introduced a new framework called GOOSE that helps learn domain knowledge from small tasks to apply to larger ones. |
Keywords
» Artificial intelligence » Optimization
