Teaching AI Algorithms with Games Including Mahjong and FightTheLandlord on the Botzone Online Platform

Wenxin Li, Haoyu Zhou, Chris Wang, Haifeng Zhang, Xingxing Hong, Yushan Zhou, Qinjian Zhang

Abstract: This paper presents a course design, named Algorithms in Game AI, as an undergraduate elective course. The course mainly focuses on common and state-of-the-art algorithms in the game AI area, including game tree based algorithms and reinforcement learning. Powered by Botzone, our game AI platform, we designed different types of assignments for this course to bring a rich and fun learning experience. We chose several games including two popular Chinese classic ones, namely Mahjong and FightTheLandlord, which are both collaborative, stochastic and partially observable. To our best knowledge, it is the first time to adopt these games in AI courses, thus providing a new benchmark for game AI education. To encourage participation and reduce frustration, milestone-based competitions and bonus tasks were adopted. In this paper, we present the structure, teaching tools, techniques, performance and findings of this course. By reviewing students' performance and feedback, we found that students enjoyed the games we provided. We also found that reinforcement learning algorithms did not perform as well as other algorithms in limited time and resources.