This is a repo including all projects and labs in my Artificial Intelligence course (DATA130008.01) in School of Data Science @Fudan University.

There is a cheat sheet of this course, and you can have a brief look about what we learn this term.

NOTICE: Requirements and code listed may be outdated, please refer to course website to see latest news.

• 1. Search in Pac-Man
• 2. N Queens
• 3. BlackJack
• 4. Car
• Final. Gomoku Competition

• 1. Uniform Cost Search
• 2. Alpha-Beta pruning
• 3. Reinforcement Learning
• 4. BayesNet

• Adapted from the Berkeley Pac-Man Assignments originally created by John DeNero and Dan Klein.
• This project is aimed at designing a intelligent Pacman agent that is able to find optimal paths through its maze world considering both reaching particular locations (e.g., finding all the corners) and eating all the dots in as few steps as possible. It can be separated as two subtasks: implementing graph search algorithms for DFS, BFS, UCS as well as A*, and use the search criteria outlined in the lectures to design effective heuristics.
• The detailed requirements are here.
• Report of this project is here.

• Adapted from Course Scheduling of Stanford CS221.
• It's a programming assignment for CSP.
• The detailed requirements are here.

• Adapted from Course Scheduling of Stanford CS221.
• It's a programming assignment for Markov Decision Process and Reinforcement Learning.
• The detailed requirements are here.

• Adapted from Course Scheduling of Stanford CS221.
• It's a programming assignment for xxx.
• The detailed requirements are here.

• Gomoku Competition is a competition of artificial intelligences playing gomoku.
• The aim of competitors is to program a computer artificial intelligence (AI) playing the gomoku game (also called Five in a Row, Connect 5 or Gobang) as good as possible.
• The detailed requirements are here.

• Implement a framed UCS code and complete online judge.

• Implement a framed Alpha-Beta pruning code and complete online judge.

• Implement a framed reinforcement learning code and complete online judge.

• Implement a framed BayesNet code and complete online judge.