CS188: Introduction to Artificial Intelligence

This project is a course taught at University of California, Berkeley, which is also available to the public through this link: PACMAN-AI.

Here are the project lists:

Project	Concepts	Score	Link
P1: Search	DFS BFS UCS A* Search Heuristics	Total: 26/25	SEARCH
Mini-Contest 1: Multi-Agent Pacman	BFS A* Search Heuristics	Varies	MINI-CONTEST-1
P2: Multi-Agent Search	Minimax Alpha-Beta Pruning Expectimax Evaluation Function	Total: 25/25	MULTI-AGENT-SEARCH
Mini-Contest 2: Multi-Agent Adversarial Pacman	Evaluation Function	Varies	MINI-CONTEST-2
P3: Reinforcement Learning	MDPs Policy Evaluation Value Iteration Q-Learning Approximate Q-Learning	Total: 25/25	REINFORCEMENT-LEARNING
P4: Ghostbusters	Hidden Markov Model Bayes Net Particle Filtering	Total: 25/25	GHOSTBUSTERS
P5: Machine Learning	Perceptron Neural Network	Total: 25/25	MACHINE LEARNING

License Information

• Licensing Information: You are free to use or extend these projects for educational purposes provided that

  (1) you do not distribute or publish solutions,
  (2) you retain this notice, and
  (3) you provide clear attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
  

• Attribution Information: The Pacman AI projects were developed at UC Berkeley. The core projects and autograders were primarily created by John DeNero ([email protected]) and Dan Klein ([email protected]). Student side autograding was added by Brad Miller, Nick Hay, and Pieter Abbeel ([email protected]).