Pacman Game – AI Search Project

This project is a simplified implementation of the classic Pacman game using Python, with optional styling via CSS.
It serves as an educational framework for experimenting with AI search algorithms, commonly taught in artificial intelligence courses.

Project Goals

• Provide a playable Pacman game as a base environment
• Allow students and developers to implement and test classic AI search algorithms
• Encourage learning through hands-on experience in a game setting
• Promote clean, modular, and documented code for easy extension

Features

• Playable Pacman game in Python
• Clearly defined sections for adding AI algorithms
• Optional CSS styling to enhance visuals
• Modular structure to support scalability and readability

Suggested AI Algorithms to Implement

You can experiment by implementing and comparing various search strategies:

• Depth-First Search (DFS).
• Breadth-First Search (BFS).
• Uniform Cost Search (UCS).
• A-Star Search (A*).
• Greedy Best-First Search.
• Bidirectional Search.
• Custom AI pathfinding logic.

TODO:

Code areas that need to be completed are marked like this:

"your code here"

Controls

⬆️ Arrow Up – Move Up

⬇️ Arrow Down – Move Down

⬅️ Arrow Left – Move Left

➡️ Arrow Right – Move Right

Requirements

• Python 3.8+

• pygame (for rendering)

• numpy (optional, for data handling)

• Install dependencies with:

 pip install -r requirements.txt