This project is taken from Berkeley's CS188 class. You can find more information about it here.
Slides on Uniform Cost Search can be found here.
Slides on A* can be found here
Slides on MiniMax can be found here
Slides on Alpha-Beta Pruning can be found here
Search
Implement depth-first, breadth-first, uniform cost, and A* search algorithms.
Used to solve navigation and traveling salesman problems in the Pacman world.
Files you'll edit (There are lots of comments to help guide you):
search.py Where all of your search algorithms will reside.
searchAgents.py Where all of your search-based agents will reside.
You can run bfs using these commands:
python pacman.py -l tinyMaze -p SearchAgent
python pacman.py -l mediumMaze -p SearchAgent
python pacman.py -l bigMaze -z .5 -p SearchAgent
If you want to run bfs add the "-a fn=bfs", e.g. python pacman.py -l mediumMaze -p SearchAgent -a fn=bfs
Learn more here: http://ai.berkeley.edu/search.html
Check out Questions 1 and 2. They give further advice and clear things up a bit.
Multiagent
Implement the Minimax and Alpha-Beta Pruning algorithms. (You can also try your hand at Expectimax algorithm)
Used to make Pacman aware of the dangers around him.
Files you'll edit/look at (There are lots of comments to help guide you):
multiAgents.py Where all of your multi-agent search agents will reside.
pacman.py The main file that runs Pacman games. This file also describes a Pacman GameState type, which you will use extensively in this project
game.py The logic behind how the Pacman world works. This file describes several supporting types like AgentState, Agent, Direction, and Grid.
util.py Useful data structures for implementing search algorithms.
Learn more here: http://ai.berkeley.edu/multiagent.html
We are interested in Questions 2 and 3.