Summary of Towards High-level Modelling in Automated Planning, by Carla Davesa Sureda et al.
Towards High-Level Modelling in Automated Planning
by Carla Davesa Sureda, Joan Espasa Arxer, Ian Miguel, Mateu Villaret Auselle
First submitted to arxiv on: 9 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 In this paper, researchers aim to enhance the expressivity of automated planning systems by developing a Python library called Unified-Planning (UP). They propose an extension to the UP library that enables the modeling of complex problems through the addition of new data types and features. Specifically, they introduce array type, expression for counting booleans, and support for integer parameters in actions. The authors demonstrate how these enhancements enable natural high-level models of three classical planning problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making it easier to use computers to plan out tasks. Planning is important because we do it every day to achieve goals. Right now, there’s a special language called PDDL that helps computers understand what they need to do. But sometimes this language isn’t good enough, so the researchers are working on a new tool to help make planning easier. They’re making some changes to an existing library called Unified-Planning (UP) to allow for more complex problems. This will help computers solve harder tasks and make better plans. |
