Summary of Return to Tradition: Learning Reliable Heuristics with Classical Machine Learning, by Dillon Z. Chen et al.
Return to Tradition: Learning Reliable Heuristics with Classical Machine Learning
by Dillon Z. Chen, Felipe Trevizan, Sylvie Thiébaux
First submitted to arxiv on: 25 Mar 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 presents a novel approach to learning for planning, aiming to achieve competitive performance against classical planners in several domains. The authors construct graph representations of lifted planning tasks using the WL algorithm and generate features from them. These features are used with classical machine learning methods that have fewer parameters and train faster than state-of-the-art deep learning models. The resulting model, WL-GOOSE, reliably learns heuristics from scratch and outperforms existing approaches in various domains. The authors also explore connections between their method and previous architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to create a better way for computers to plan and make decisions. It uses special graphs and algorithms to help machines learn how to solve problems more effectively. The new approach, called WL-GOOSE, is able to learn from scratch and do as well or even better than other existing methods. This is important because it could lead to improvements in areas like logistics, transportation, and more. |
Keywords
» Artificial intelligence » Deep learning » Machine learning
