The Pac-Man Projects

This short UNIX/Python tutorial introduces students to the Python programming language and the UNIX environment.

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.

Students will apply the search algorithms and problems implemented in Project 1 to handle more difficult scenarios that include controlling multiple pacman agents and planning under time constraints

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.

This minicontest involves a multiplayer capture-the-flag variant of Pacman, where agents control both Pacman and ghosts in coordinated team-based strategies. Each team will try to eat the food on the far side of the map, while defending the food on their home side.

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 the perceptron algorithm and neural network models, and apply the models to several tasks including digit classification.

Students create strategies for a team of two agents to play a multi-player capture-the-flag variant of Pacman.