Summary of Graph Learning For Numeric Planning, by Dillon Z. Chen et al.
Graph Learning for Numeric Planning
by Dillon Z. Chen, Sylvie Thiébaux
First submitted to arxiv on: 31 Oct 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 A novel machine learning approach is introduced for solving numeric planning tasks, which extends symbolic planning to include numerical variables. The proposed models leverage graph learning’s ability to exploit relational structures and handle arbitrary object numbers. A new graph kernel is designed for graphs with continuous and categorical attributes, accompanied by optimized methods for learning heuristic functions. Experimental results demonstrate that the graph kernels outperform graph neural networks in terms of efficiency and generalization, while also achieving competitive coverage performance compared to domain-independent numeric planners. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a way to use machine learning to solve planning problems that involve numbers. Planning is like solving puzzles, but with rules and objects. The new approach uses graphs, which are helpful for recognizing patterns in relationships between things. It’s more efficient and good at generalizing than other methods. The results show that it can be used to solve a variety of planning tasks quickly and accurately. |
Keywords
» Artificial intelligence » Generalization » Machine learning
