Welcome to the Pacman AI project, an academic exploration into artificial intelligence through a practical application. This project is designed as part of a university coursework "Artificial Intelligence" lectured by Prof. Dr. Jakob Suchan to delve into the complexities of AI by implementing intelligent agents within the classic Pacman game framework. The focus is on developing algorithms that enable the player to exhibit advanced behaviors such as strategic chasing, evading, and interacting under various game conditions. This implementation serves not only as an educational tool but also as a challenging platform to test and enhance our understanding of AI methodologies in dynamic environments.
Pacman_AI/
├── Assets
│ ├── ghost_images # Contains images for the different ghosts
│ └── player_images # Contains images for the player character
├── board.py # Defines the game board configurations
└── pacman.py # Main game logic including AI behaviors for ghosts
For this phase, ensure your project environment is set up to build your own rational agents. The environment should include structures such as walls or obstacles and allow the agent to move around and accomplish specific tasks.
Relation to Project: This phase is crucial as it establishes the basic functionality where Pacman operates as a rational agent, responding to the game environment dynamically. The setup involves programming the Pacman to recognize and react to static elements like walls, which is a fundamental aspect of AI known as perception-action cycles.
Use search algorithms to solve tasks within the environment, such as navigating a maze or carrying items from one place to another. This phase allows for experimenting with both uninformed and informed search strategies. Be creative in task design.
Relation to Project: This stage directly addresses the AI aspect of pathfinding and movement strategy. By experimenting with different search algorithms, we can analyze how these approaches affect Pacman’s efficiency and effectiveness in navigating the maze and avoiding ghosts. This not only enhances the AI's adaptability to changing game states but also its overall strategic planning.
Introduce interactions between agents within the environment. This could involve competitive interactions using adversarial search or cooperative tasks where multiple agents work together on a distributed task.
Relation to Project: This phase deepens the complexity of AI interactions by simulating real-time decision-making against intelligent opponents. It simulates a mini-ecosystem where different AI agents with varying objectives must interact, compete, and possibly cooperate. This mimics real-world AI applications where multiple systems converge, requiring both competition and cooperation, thus pushing the boundaries of what AI can achieve in dynamic and unpredictable environments.
To run this project, you will need Python and Pygame installed on your system.
- Python 3.6
- Pygame library
You can install Pygame using pip:
pip install pygameNavigate to the project directory and run the pacman.py script:
python pacman.py- Player Controls: Use the arrow keys to move Pacman through the maze.
- Ghosts: Ghosts have different behaviors and use AI to chase or evade Pacman based on the current game state.
Feel free to fork this project and contribute by adding new features or improving the existing game mechanics. Pull requests are welcome.
This project is open-source and available under the MIT License.
- Mohamed Mourad Ouazghire 30005138
- Nadim Khneisser 30006165
- Vaya Parth Jitendra
- Dayanna Picon