MANNY

This project is based on The Pac-Man projects developed by John DeNero, Dan Klein, and Pieter Abbeel at UC Berkeley.

We implement artifical intelligence of agents in Pac-Man world. Use commands below to run the client with the desired algorithm.

You should be able to play a game of Pac-Man by typing the following at the command line:

$ python pacman.py

Search

Depth First Search

$ python pacman.py -l tinyMaze -p SearchAgent
$ python pacman.py -l mediumMaze -p SearchAgent
$ python pacman.py -l bigMaze -z .5 -p SearchAgent

Breadth First Search

$ python pacman.py -l mediumMaze -p SearchAgent -a fn=bfs
$ python pacman.py -l bigMaze -p SearchAgent -a fn=bfs -z .5

Uniform Cost Search

$ python pacman.py -l mediumMaze -p SearchAgent -a fn=ucs
$ python pacman.py -l mediumDottedMaze -p StayEastSearchAgent
$ python pacman.py -l mediumScaryMaze -p StayWestSearchAgent

A* Search

$ python pacman.py -l bigMaze -z .5 -p SearchAgent -a fn=astar,heuristic=manhattanHeuristic

Multi-Agent Search

Reflex Agent

$ python pacman.py -p ReflexAgent -l testClassic
$ python pacman.py --frameTime 0 -p ReflexAgent -k 1
$ python pacman.py --frameTime 0 -p ReflexAgent -k 2

Minimax

$ python pacman.py -p MinimaxAgent -l minimaxClassic -a depth=4
$ python pacman.py -p MinimaxAgent -l trappedClassic -a depth=3

Alpha-Beta Pruning

$ python pacman.py -p AlphaBetaAgent -a depth=3 -l smallClassic
$ python pacman.py -p AlphaBetaAgent -l trappedClassic -a depth=3 -q -n 10

Expectimax

$ python pacman.py -p ExpectimaxAgent -l minimaxClassic -a depth=3
$ python pacman.py -p ExpectimaxAgent -l trappedClassic -a depth=3 -q -n 10

Evaluation

These are the command for obtaining evaluation results presented in the paper.

Open Classic Maze

$ python pacman.py -p ReflexAgent -l openClassic -q -n 10
$ python pacman.py -p MinimaxAgent -l openClassic -a depth=3 -q -n 10
$ python pacman.py -p AlphaBetaAgent -l openClassic -a depth=3 -q -n 10
$ python pacman.py -p ExpectimaxAgent -l openClassic -a depth=3 -q -n 10

Small Classic Maze

$ python pacman.py -p ReflexAgent -l smallClassic -q -n 10
$ python pacman.py -p MinimaxAgent -l smallClassic -a depth=3 -q -n 10
$ python pacman.py -p AlphaBetaAgent -l smallClassic -a depth=3 -q -n 10
$ python pacman.py -p ExpectimaxAgent -l smallClassic -a depth=3 -q -n 10

Medium Classic Maze

$ python pacman.py -p ReflexAgent -l mediumClassic -q -n 10
$ python pacman.py -p MinimaxAgent -l mediumClassic -a depth=3 -q -n 10
$ python pacman.py -p AlphaBetaAgent -l mediumClassic -a depth=3 -q -n 10
$ python pacman.py -p ExpectimaxAgent -l mediumClassic -a depth=3 -q -n 10

Tricky Classic Maze

$ python pacman.py -p ReflexAgent -l trickyClassic -q -n 10
$ python pacman.py -p MinimaxAgent -l trickyClassic -a depth=3 -q -n 10
$ python pacman.py -p AlphaBetaAgent -l trickyClassic -a depth=3 -q -n 10
$ python pacman.py -p ExpectimaxAgent -l trickyClassic -a depth=3 -q -n 10

Minimax Classic Maze

$ python pacman.py -p ReflexAgent -l minimaxClassic -q -n 10
$ python pacman.py -p MinimaxAgent -l minimaxClassic -a depth=3 -q -n 10
$ python pacman.py -p AlphaBetaAgent -l minimaxClassic -a depth=3 -q -n 10
$ python pacman.py -p ExpectimaxAgent -l minimaxClassic -a depth=3 -q -n 10

Trapped Classic Maze

$ python pacman.py -p ReflexAgent -l trappedClassic -q -n 10
$ python pacman.py -p MinimaxAgent -l trappedClassic -a depth=3 -q -n 10
$ python pacman.py -p AlphaBetaAgent -l trappedClassic -a depth=3 -q -n 10
$ python pacman.py -p ExpectimaxAgent -l trappedClassic -a depth=3 -q -n 10

mkubilayk / manny