As part of UC Berkeley's introductory Artificial Intelligence Course, CS 188, the Pac-Man projects were developed to help students understand the lesson material better. You can find more information on their website.
There are 5 projects in total, each focusing on specific segments of the course:
Students implement depth-first, breadth-first, uniform cost, and A* search algorithms. These algorithms are used to solve navigation and traveling salesman problems in the Pacman world.
Classic Pacman is modeled as both an adversarial and a stochastic search problem. Students implement multiagent minimax and expectimax algorithms, as well as designing evaluation functions.
Students implement model-based and model-free reinforcement learning algorithms, applied to the AIMA textbook's Gridworld, Pacman, and a simulated crawling robot.
Probabilistic inference in a Hidden Markov Model tracks the movement of hidden ghosts in the Pacman world. Students implement exact inference using the forward algorithm and approximate inference via particle filters.
Students implement standard machine learning classification algorithms using Naive Bayes, Perceptron, and MIRA models to classify digits. Students extend this by implementing a behavioral cloning Pacman agent.