<<<<<<< HEAD
Project Link : http://ai.berkeley.edu/project_overview.html
Sections Of the Project Covered are:
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.
Ghostbusters: Probabilistic inference in a hidden Markov model tracks the movement of hidden ghosts in the Pacman world. Implement exact inference using the forward algorithm and approximate inference via particle filters.
Artificial Intelligence project designed by UC Berkeley to develop game agents for Pacman using search algorithms and reinforcement learning
b747ae092562a96c8c2ac70466fc10707a0f6865