Let's create a more enjoyable world together by utilizing games and AI

Games and Artificial Intelligence Lab
Senior lecturer：HSUEH Chu-Hsuan

E-mail：
［Research areas］
Game Informatics
［Keywords］
Board/Video Game, Puzzle, Tree Search, Supervised/Reinforcement Learning, Strong/Entertaining Game AI

Skills and background we are looking for in prospective students

Most important: have interests in and passions for the research of games and AI. Required: basic knowledge of probability/statistics and programming skills. Preferred: experience in game AI development or machine learning

What you can expect to learn in this laboratory

Students are expected to acquire advanced techniques in the field of games and AI. Additionally, students develop the ability to identify and solve problems, to survey, organize, and summarize related work, and to formulate and verify hypotheses. They also develop logical and critical thinking skills, as well as the ability to clearly explain their ideas and results using appropriate text and figures. Furthermore, students are expected to gain the ability to write technical papers and reports following standard conventions in the field. Understanding and complying with research and information ethics throughout the research process is also an important quality to be acquired.

【Job category of graduates】 Expected to be SEs and data scientists in IT companies, planners and programmers in game companies

Research outline

[Overview]

Games have long been a part of human entertainment and one of the key fields of AI research. In many games, AI players have become stronger than top human players. While powerful and general AI methods are actively studied, researchers also focus on making AI more accessible to humans as society moves toward Society 5.0. We work on these two directions of AI research in games.
* We do not conduct research on designing games.

[Strong AI Players/Solving Games]

We work on creating strong AI players using tree search and reinforcement learning (especially AlphaZero). We have achieved good results in game competitions.
We are also interested in finding out optimal strategies and theoretical values of games (so-called game solving). A simple example is that if both players play optimally in tic-tac-toe, the game ends in a draw.

[Human-centered game AI]

1. Teaching Games
We aim to let AI perform teaching games, one of the methods used by human teachers to teach Go (figure on the right). To achieve this, we research on playing human-like moves, playing good-quality games, and pointing out bad moves. Another goal is to generate explanations or comments on the game states or moves.

2. Procedural Content Generation
Game content refers to various things: maps, music, characters, weapons, puzzles, etc. We work on automatic generation of content that is fun to play or becomes good practice. Examples include the generation of mazes that guide human players, puzzles to practice the T-spin technique in Tetris, and puzzles for Go beginners and intermediate players (motivations in figure below).

Key publications

1. Chu-Hsuan Hsueh, Kyota Kuboki, Shi-Jim Yen, and Kokolo Ikeda, “ Making KataGo HumanSL More Human-Like for Amateur-Level Play”, 19th Advances in Computer and Games, Oct. 2025
2. Chu-Hsuan Hsueh, Takefumi Ishii, Tsuyoshi Hashimoto, and Kokolo Ikeda, “ Proposal and Generation of Endgame Puzzles for an Imperfect Information Game Geister”,  Entertainment Computing, vol. 52, Jan. 2025
3. Chu-Hsuan Hsueh and Kokolo Ikeda, “Improvement of Move Naturalness for Playing Good-Quality Games with Middle-Level Players”, Applied Intelligence, vol. 54, pp. 1637-1655, Jan. 2024