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/
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 .5https://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