Artificial-Intelligence---The-PacMan-Projects

This project work is a part of Artificial Intelligence coursework at the University of Oulu.

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 the Pacman world. Use commands below to run the client with the desired algorithm.

Getting started

Follow the instructions below to get started.

Prerequisites

The code is written in Python 2.7.

Running the program

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

$ python pacman.py

Built with

• Python 2.7

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.

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

Author

• Md Shariful Alam - Shariful

Acknowledgments

• The UniOulu AI course and its teachers
• The Berkeley AI course - For providing the original materials!
• The University of Oulu - For providing this awesome course!