Welcome to the Pac-Man AI Projects repository, created as part of the coursework at the University of Oulu. This repository contains implementations of various AI techniques applied to solve challenges within the Pac-Man game environment. These projects are based on the original Pac-Man AI Projects developed for UC Berkeley's CS 188 course.
This project series covers fundamental AI concepts through engaging and interactive Pac-Man environments. The key topics and algorithms implemented include:
Implementations of classic search algorithms:
- Depth-First Search (DFS)
- Breadth-First Search (BFS)
- Uniform Cost Search (UCS)
- A* Search
Exploring adversarial and stochastic search problems by modeling classic Pac-Man as a multi-agent environment:
- Minimax Algorithm
- Expectimax Algorithm
- Evaluation Functions
- Visualization of AI Techniques: Students can observe the outcomes of their implemented algorithms in a dynamic and interactive environment.
- Hands-on AI Experience: Provides practical experience with key AI algorithms, accompanied by clear instructions and examples.
- Creative Problem Solving: Pac-Man offers a challenging problem space that requires innovative solutions, preparing students for real-world AI challenges.
- Programming Language: Python 3.10
- Python 3.10 installed on your system.
- Familiarity with basic Python programming and AI concepts.
- This project is based on the Pac-Man AI Projects created by UC Berkeley for their CS 188 course.
- Supported by the National Science Foundation (NSF) under CAREER grant 0643742.