Instructor: Dr. M. Javanmardi

Semester: Fall 2021

Introduction

In this project, your Pacman agent will find paths through his maze world, both to reach a particular location and to collect food efficiently. You will build general search algorithms and apply them to Pacman scenarios.

As in the Coding Diagnostic, this project includes an autograder for you to grade your answers on your machine. This can be run with the command:

python3 autograder.py

Welcom to Packman

Pacman lives in a shiny blue world of twisting corridors and tasty round treats. Navigating this world efficiently will be Pacman’s first step in mastering his domain.

• Search: 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.

• Multi-Agent Search: Classic Pacman is modeled as both an adversarial and a stochastic search problem. Implement multiagent minimax and expectimax algorithms, as well as designing evaluation functions.

• Reinforcement Learning: Implement model-based and model-free reinforcement learning algorithms, applied to the AIMA textbook's Gridworld, Pacman, and a simulated crawling robot.