Summary of Generalized Planning For the Abstraction and Reasoning Corpus, by Chao Lei et al.
Generalized Planning for the Abstraction and Reasoning Corpus
by Chao Lei, Nir Lipovetzky, Krista A. Ehinger
First submitted to arxiv on: 15 Jan 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 Abstraction and Reasoning Corpus (ARC) poses challenges for machine learning methods due to its focus on reasoning and abstraction. The paper introduces an ARC solver, Generalized Planning for Abstract Reasoning (GPAR), which casts ARC problems as generalized planning (GP) problems. GPAR uses the standard Planning Domain Definition Language (PDDL) with external functions representing object-centric abstractions. Experiments show that GPAR outperforms state-of-the-art solvers on object-centric tasks, demonstrating the effectiveness of GP and PDDL’s expressiveness in modeling ARC problems. The challenges provided by ARC motivate research to advance existing GP solvers and understand new relations with other planning computational models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a special test for artificial intelligence that’s really hard because it requires good reasoning and thinking. They created a way to solve this test using something called Generalized Planning, which is like making a plan to find the answer. They used a special language to help with this process. The results show that their method works better than other methods, which means it can be used to make artificial intelligence more intelligent. |
Keywords
» Artificial intelligence » Machine learning
