cs188

CS 188 Artificial Intelligence

This repo contains the 5 projects I did for CS188.

1. 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.

My work:

• search.py: Where all of search algorithms resides.
• searchAgents.py: Where all of search-based agents reside.

2. 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.

My work:

• multiAgents.py: Where all of multi-agent search agents reside.

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

My work:

• valueIterationAgents.py: A value iteration agent for solving known MDPs.
• qlearningAgents.py: Q-learning agents for Gridworld, Crawler and Pacman.

4. BNs and HMMs: 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.

My work:

• bustersAgents.py: Agents for playing the Ghostbusters variant of Pacman.
• inference.py: Code for tracking ghosts over time using their sounds.

5. Machine Learning: Classification implement the perceptron algorithm and neural network models, and apply the models to several tasks including digit classification.

My work:

• models.py: Perceptron and neural network models for a variety of applications