![]() ![]() Students create strategies for a team of two agents to play a multi-playerĬapture-the-flag variant of Pacman. Implementing a behavioral cloning Pacman agent. Naive Bayes, Perceptron, and MIRA models to classify digits. Students implement standard machine learning classification algorithms using Students implement exact inference using the forwardĪlgorithm and approximate inference via particle filters. Probabilistic inference in a hidden Markov model tracks the movement of hidden Important Links Instructor's Guide for Projects Using the Projects on the edX Platform Contact Information Projects Overview We are now happy to release them to other universities for educational use. The projects have been field-tested, refined, and debugged over multiple semesters at Berkeley. In our course, these projects have boosted enrollment, teaching reviews, and student engagement. Finally, Pac-Man provides a challenging problem environment that demands creative solutions real-world AI problems are challenging, and Pac-Man is too. They also contain code examples and clear directions, but do not force students to wade through undue amounts of scaffolding. The projects allow students to visualize the results of the techniques they implement. ![]() We designed these projects with three goals in mind. These concepts underly real-world application areas such as natural language processing, computer vision, and robotics. Instead, they teach foundational AI concepts, such as informed state-space search, probabilistic inference, and reinforcement learning. However, these projects don't focus on building AI for video games. They apply an array of AI techniques to playing Pac-Man. The Pac-Man projects were developed for UC Berkeley's introductory artificial intelligence course, CS 188.
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