Summary of Introduction to Ai Planning, by Marco Aiello and Ilche Georgievski
Introduction to AI Planning
by Marco Aiello, Ilche Georgievski
First submitted to arxiv on: 16 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 abstract presents an introduction to key concepts and techniques in AI Planning, also known as Automated Planning, which emerged from the need to give autonomy to robots. The field has evolved over decades, with various approaches developed for specific tasks and applications. Most approaches represent the world as a state within a state transition system, where the planning problem becomes searching a path from the current state to one satisfying user goals. The abstract discusses classical planning, fundamental algorithms, planning as a constraint satisfaction problem, and Hierarchical Task Network (HTN) planning, which is widely used in the field. Additionally, it touches on the Planning Domain Definition (PDDL) Language, the standard syntax for representing non-hierarchical planning problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI Planning is an area of research that helps robots make decisions to achieve goals. It started back in 1966 when we wanted robots to be more autonomous. Over time, many different approaches were developed to solve specific problems. One common way to plan is by looking at the world as a collection of states and finding a path from where you are now to where you want to go. This abstract explains some of the basics of AI Planning, including classical planning, algorithms for solving problems, and how to use Hierarchical Task Network (HTN) planning. It also covers the language we use to define planning problems. |
Keywords
» Artificial intelligence » Syntax
