Summary of Ai Planning: a Primer and Survey (preliminary Report), by Dillon Z. Chen et al.
AI Planning: A Primer and Survey (Preliminary Report)
by Dillon Z. Chen, Pulkit Verma, Siddharth Srivastava, Michael Katz, Sylvie Thiébaux
First submitted to arxiv on: 7 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 The proposed paper aims to bridge the gap between various AI sub-disciplines by providing a primer on classical AI planning (AP) concepts and techniques. Specifically, it introduces the Markov Decision Process formalism for handling uncertainty and time in AP problems. The paper surveys state-of-the-art methods for solving AP problems, focusing on their ability to exploit problem structure. Additionally, it covers subfields within AP for learning structure from unstructured inputs and generalizing to unseen scenarios. This work can potentially contribute to the development of more effective automated decision-making systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps connect different areas of AI research by explaining classical ideas in AI planning (AP). It starts with the basics of AP problems and how they’re represented, then shows how to handle uncertainty and time using a special way of thinking called Markov Decision Processes. The authors also look at what’s currently being done well in this area and how it can be used to solve more complex problems. |
