Summary of Projection Abstractions in Planning Under the Lenses Of Abstractions For Mdps, by Giuseppe Canonaco et al.
Projection Abstractions in Planning Under the Lenses of Abstractions for MDPs
by Giuseppe Canonaco, Alberto Pozanco, Daniel Borrajo
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 This paper aims to unify the concepts of abstraction in AI Planning and discounted Markov Decision Processes (MDPs). Despite commonalities between the two fields, their approaches to building and using abstractions differ. The authors aim to bridge this gap by relating projection abstractions in Planning to discounted MDPs. They demonstrate how a single abstraction can be obtained using both Planning techniques and discounted MDP frameworks. The paper highlights computational and representational advantages and disadvantages of both approaches, opening up new research directions for both fields. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers are trying to connect two different areas of AI: AI Planning and Markov Decision Processes (MDPs). They’re looking at how we create “abstractions” in each area. Abstractions help us simplify complex problems by focusing on the most important details. The authors want to show that even though these areas seem separate, they can learn from each other. They’ll start with a special kind of abstraction used in AI Planning and then show how it can be applied to MDPs too. By comparing the two approaches, the researchers hope to find new ways to solve problems in both fields. |
