An array of AI techniques is employed to playing Pac-Man 
. Following Informed, Uninformed and Adversarial Search algorithms are implemented in this project.
- Informed Search:
- Breadth First Search
- Depth First Search
- Uniform Cost Search
- Uninformed Search:
- A* Search
- Adversarial Search:
- Minimax Search
- Alpha-Beta Pruning
Expand deepest node.
cd Informed and Uninformed Search
python pacman.py -l mediumMaze -p SearchAgent -z .8 --frameTime 0.05
Expand shallowest node.
cd Informed and Uninformed Search
python pacman.py -l mediumMaze -p SearchAgent -a fn=bfs -z .8 --frameTime 0.05
Expand least cost node.
cd Informed and Uninformed Search
python pacman.py -l mediumMaze -p SearchAgent -a fn=ucs -z .8 --frameTime 0.05
Minimize the total estimated solution cost.
cd Informed and Uninformed Search
python pacman.py -l mediumMaze -p SearchAgent -a fn=astar,heuristic=manhattanHeuristic -z .8 --frameTime 0.05
Max maximizes results, Min minimizes results. Compute each node’s minimax value’s the best achievable utility against an optimal adversary.
cd Adversarial Search
python pacman.py -p MinimaxAgent -l smallClassic -a depth=2 --frameTime 0
If you lose, try increasing depth because depth matters.Minimax: generates the entire game search space. Alpha-Beta algorithm: prune large chunks of the trees.
cd Adversarial Search
python pacman.py -p AlphaBetaAgent -l smallClassic -a depth=3 --frameTime 0
If you lose, try increasing depth because depth matters.UC Berkeley's introductory artificial intelligence course, CS 188.