PacMan Projects

The PacMan projects were developed for CS 188. They apply an array of AI techniques to playing PacMan.

https://inst.eecs.berkeley.edu/~cs188/fa20/projects/

Project 1: Search

https://inst.eecs.berkeley.edu/~cs188/fa20/project1/

In this project, your Pacman agent will find paths through his maze world, both to reach a particular location and to collect food efficiently. I build general search algorithms and apply them to Pacman scenarios.

Questions:

• Finding a Fixed Food Dot using Depth First Search

python pacman.py -l mediumMaze -p SearchAgent

• Breadth First Search

python pacman.py -l mediumMaze -p SearchAgent -a fn=bfs

• Varying the Cost Function

python pacman.py -l mediumMaze -p SearchAgent -a fn=ucs

• A* search

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

• Finding All the Corners

python pacman.py -l mediumCorners -p SearchAgent -a fn=bfs,prob=CornersProblem

• Corners Problem: Heuristic

python pacman.py -l mediumCorners -p AStarCornersAgent -z 0.5

• Eating All The Dots

python pacman.py -l testSearch -p SearchAgent -a fn=astar,prob=FoodSearchProblem,heuristic=foodHeuristic

• Suboptimal Search

python pacman.py -l bigSearch -p ClosestDotSearchAgent -z .5

Project 2: Multi-Agent Search

https://inst.eecs.berkeley.edu/~cs188/fa20/project2/

In this project, I designed agents for the classic version of Pacman, including ghosts. Along the way, I implemented both minimax and expectimax search and try hand at evaluation function design.

Questions:

• Reflex Agent

python pacman.py -p ReflexAgent -l testClassic

• Minimax

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

• Alpha-Beta Pruning

python pacman.py -p AlphaBetaAgent -a depth=3 -l smallClassic

• Expectimax

python pacman.py -p ExpectimaxAgent -l minimaxClassic -a depth=3

• Evaluation Function