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Kaneko Lab
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A lab that builds smarter AI through games such as shogi, Catan, and Werewolf.
AboutAbout the lab
Making AI smarter through games.
Kaneko Lab is a game AI lab that studies artificial intelligence (AI) using games as its subject.
Games have long been studied as a good environment for testing how smart an AI is.
Today, the best AI surpasses humans not only in Go and shogi but also in games like poker, where players cannot see their opponents' hands.
The lab studies the ability of AI to understand the world and other agents through challenges such as reinforcement learning, search, and negotiation and communication in games like Werewolf and Catan.
Key terms
- Reinforcement learning
- A machine learning method in which an agent gradually acquires better behavior based on the rewards obtained as a result of trial and error
- Game record (kifu)
- A record of the moves played in a game such as shogi or Go, in the order they were played
- Monte Carlo tree search
- A method of finding good moves by evaluating future positions in a game through repeated random playouts
NewsLab news
An undergraduate student's graduation thesis received the Ichiko Memorial Award
A master's student's master's thesis received the Department of Multidisciplinary Sciences Encouragement Award
An alumnus received the IPSJ Yamashita SIG Research Award
Students received awards including the Best Paper Award at the Game Programming Workshop 2023
ResearchResearch themes
Games have long been studied as an excellent evaluation environment for artificial intelligence. The lab studies reinforcement learning and search methods using a wide variety of games that differ in perfect or imperfect information, number of players, and the nature of rewards.
*Illustrative imageAI for a Variety of Games
The suitable methods and the level of difficulty change greatly depending on the properties of a game, such as whether the opponents' information is visible, how many players take part, and the nature of the rewards. The lab works with games such as Catan, which has multiple players and an element of negotiation, and Werewolf, which involves inference and persuasion.
*Illustrative imageReinforcement Learning
The lab studies reinforcement learning in games. It takes on the challenge of learning only from experience gained through the rules of a game and simulators, without using human knowledge or game records.
*Illustrative imageSearch
The lab studies methods for searching efficiently by coordinating many computers. It also works on improving the performance of Monte Carlo tree search, approaching it from both theory and practice.
*Illustrative imageComputer Shogi
The lab conducts research on computer shogi. At the 2nd Den-O Tournament in 2013, a match was held between GPS Shogi and a professional shogi player.
MembersFaculty
The members of Kaneko Lab consist of faculty and students in the doctoral program, the master's program, and the undergraduate program.As of 2026
- ProfessorTomoyuki KanekoPublications ↗
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Go (13×” Dietmar Rabich / CC BY-SA 4.0 (cropped)
- AI for a Variety of Games: “Gen Con - 20030724 - Giant Settlers of Catan Game” Alan De Smet / CC BY 3.0 (cropped)
- Reinforcement Learning: “Concept of Reinforcement Learning” Tsesea / CC BY-SA 4.0 (cropped)
- Search: “MCTS Algorithm” Robert Moss / CC BY-SA 4.0 (cropped)
- Computer Shogi: “Shogi board pieces and komadai” Oliver Orschiedt / CC BY-SA 3.0 (cropped)
- Related lab (University of Tokyo Baba Lab): “PVC Brainstorming Workshop ( )” BLM Nevada / Public domain (cropped)
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