Implementation of classical search algorithms in Python including:
- Depth First Search (DFS)
- Breadth First Search (BFS)
- Uniform Cost Search (UCS)
- A* Search
These algorithms were implemented as part of an Artificial Intelligence course project using the UC Berkeley Pacman AI framework.
The goal of the project was to explore how different search strategies traverse a state space and how heuristics can dramatically improve pathfinding efficiency.
Agents were tested in grid-based maze environments where Pacman must navigate to reach goal states.
DFS
Explores deep paths first before backtracking.
BFS
Explores nodes level by level to guarantee shortest paths in unweighted graphs.
UCS
Expands nodes with the lowest cumulative path cost.
A*
Combines path cost and heuristic estimates to efficiently reach the goal.
Python
Graph Search
Heuristic Optimization
Example runs of the agents solving maze layouts:
This project builds upon the UC Berkeley CS188 Pacman AI framework.