Instructor: Prof. Brian Ziebart
Team Members:
• Kislaya Singh: ksingh38@uic.edu
• Pratyush Bagaria: pbagar2@uic.edu
About the project:
The Pac-Man projects were developed for UC Berkeley's artificial intelligence course. The projects along with the complete course was adapted by our professor here at University of Illinois at Chicago. They apply an array of AI techniques to playing Pac-Man. However, these projects doesn't focus on building AI for video games. Instead, they teach foundational AI concepts, such as informed state-space search, probabilistic inference, and reinforcement learning. These concepts underly real-world application areas such as natural language processing, computer vision, and robotics. The projects allow us to visualize the results of the techniques implemented. Finally, Pac-Man provides a challenging problem environment that demands creative solutions; real-world AI problems are challenging, and Pac-Man is too. More information about the project can be found at - http://ai.berkeley.edu/project_overview.html
List of projects:
Project 1: Search in Pac-Man - Autograder Score: 25/25
Built general search algorithms and applied them to Pacman scenarios.
Project 2: Multiagent Pac-Man - Autograder Score: 25/25
Designed agents for the classic version of Pacman, including ghosts. Implemented both minimax and expectimax search and tried hand at evaluation function design.
Project 3: Reinforcement Learning - Autograder Score: 25/25
Implemented value iteration and Q-learning.
Project 4: Ghostbusters - Autograder Score: 25/25
Designed Pacman agents that uses sensors to locate and eat invisible ghosts.
Project 5: Classification - Autograder Score: 25/25
Designed three classifiers: a perceptron classifier, a large-margin (MIRA) classifier, and a slightly modified perceptron classifier for behavioral cloning.