Summary of Wlplan: Relational Features For Symbolic Planning, by Dillon Z. Chen
WLPlan: Relational Features for Symbolic Planning
by Dillon Z. Chen
First submitted to arxiv on: 1 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 proposed paper introduces WLPlan, a C++ package with Python bindings that aims to provide scalable learning planners for planning research. The tool is motivated by the need for efficient handling of learning and planning modules. WLPlan implements recent work on automatically generating relational features of planning tasks, which can be used for downstream routines such as domain control knowledge or probing and understanding planning tasks. Key functionalities include graph transformation and embedding of planning graphs into feature vectors via graph kernels. The package offers a comprehensive framework for developing scalable learning planners, making it a valuable tool for the research community. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary WLPlan is a new tool that helps researchers create better plans. Planning is like creating a roadmap to solve a problem. Right now, people often use different programming languages to make their planning and learning tasks work together efficiently. WLPlan brings these two tasks together in one place, making it easier for researchers to develop scalable planners. This can help them understand planning tasks better and even control domains more effectively. The tool includes features like graph transformation and embedding, which are important steps in creating a planner. With WLPlan, researchers can focus on developing their skills instead of worrying about the technical details. |
Keywords
» Artificial intelligence » Embedding
