The Pac-Man projects were developed for UC Berkeley's introductory artificial intelligence course, CS 188. They apply an array of AI techniques to playing Pac-Man. However, these projects don't focus on building AI for video games. Instead, they teach foundational AI concepts, such as informed state-space search, probabilistic inference, and reinforcement learning. These concepts underly real-world application areas such as natural language processing, computer vision, and robotics.
- This project run on Python 3.x
- Install required packages by run the following code
pip install -r requirements.txt
- Simply type the following command
python pacman.py
- For more options
python pacman.py -h
You are welcome to use the Pac-Man projects and infrastructure for any educational or personal use. We ask only that you:
- Please do not distribute or post solutions to any of the projects.
- Please retain the attribution text at the top of each Python file.
- Talk to us before re-releasing, repacking, or extending the projects.
Additionally, if you have any questions, feedback, or bug reports about our projects, there are two ways of getting them addressed. (1) An public instructor forum through Piazza, in which you will need to contact us to get an access code to join, and (2) a private form linked here (preferably for bug reports). For more information, see the Contact section.
The lecture slides, homework and exams were developed primarily by Dan Klein and Pieter Abbeel.
The artwork was drawn by Ketrina Yim.
The Pac-Man projects were developed primarily by John DeNero and Dan Klein.
The autograder development was headed up by Nick Hay, Brad Miller, Dan Klein, and Pieter Abbeel.
Many others have contributed to the projects, including Nimar Arora, David Burkett, Jeremy Cowles, Jeff Donahue, Dan Gillick, Aria Haghighi, Judy Hoffman, Ed Karuna, Jonathan Long, Jeremy Maitin-Shepard, Barak Michener, Aditi Muralidharan, Adam Pauls, Arjun Singh, and Daniel Urieli.