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Machine Learning and Language Understanding Lab
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A lab that explores how language comes to be by studying communication between AI agents.
AboutAbout the lab
Exploring what language is through AI learning.
The Multi-Agent Learning and Language Understanding Lab researches the question "what exactly is language?" from a computational perspective.
Within the framework of multi-agent reinforcement learning, the lab builds systems in which multiple machine-learning agents acquire communication from scratch, and analyzes the conditions and mechanisms under which it becomes language-like.
As ways of approaching this question, the lab also studies learning methods that promote the establishment of cooperation and norms, and methods for interpreting the internal mechanisms of AI.
In April 2026, the lab changed its name from "Machine Learning and Language Understanding Lab" to "Multi-Agent Learning and Language Understanding Lab."
For graduation research, the lab accepts students from the College of Media Arts, Science and Technology and the College of Knowledge and Library Sciences.
Key terms
- Multi-agent reinforcement learning
- A machine learning framework in which multiple AIs (agents) learn behavior by trial and error, using rewards as a guide
- Signaling game
- A framework in which multiple symbols are exchanged. Used to analyze the structure of emergent signals
- Social dilemma
- A situation in which the interests of individual agents and the interests of the group as a whole do not align
NewsLab news
Open lab held for students who will join the lab for graduation research in FY2027 (10/13)
Won the Young Achievement Award from the Database Society of Japan at DEIM2026
Won the Student Presentation Award at DEIM2024
Won the Annual Conference Excellence Award at JSAI2023
ResearchResearch themes
The lab uses machine learning to try to answer the question "what does it mean to understand language?" It also considers how the findings from this question can contribute to the development of artificial intelligence (AI) technologies that handle language.
*Illustrative imageResearch on emergent communication
The lab studies the process by which multiple machine-learning agents acquire means of communication from scratch through interaction. Using signaling games, it also analyzes the conditions under which emergent signals have a structure that is easy to interpret, like human language.
*Illustrative imageResearch on the conditions that give rise to cooperative behavior
The lab studies how cooperative behavior and norms form and become established in social dilemma environments, where the interests of individuals and the interests of the group as a whole do not align. In environments where the consequences of actions appear over long episodes, it aims to clarify the conditions that promote cooperation and the learning dynamics of the group.
*Illustrative imageResearch on understanding the inner workings of AI
The lab studies methods for analyzing the internal mechanisms by which machine-learning models produce decisions. By examining the inside of policy models obtained through learning, it aims to clarify the mechanisms that underlie emergent communication and cooperative behavior.
MembersFaculty
The members of the Multi-Agent Learning and Language Understanding Lab consist of faculty, doctoral and master's students, undergraduate students and research students, and administrative support staff.As of 2026
- Associate ProfessorKei Wakabayashi
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: “Chess game Staunton No. 6” Wilfredor / CC0 (cropped)
- Research on emergent communication: “020118-N-6520M-011 Semaphore Flags” U.S. Navy Photo by Photographer’s Mate 1st Class Greg Messier / Public domain (cropped)
- Research on the conditions that give rise to cooperative behavior: “Bienen 35b” Waugsberg / CC BY-SA 3.0 (cropped)
- Research on understanding the inner workings of AI: “Artificial Neural Network with Chip” mikemacmarketing / photo on flickr / CC BY 2.0 (cropped)
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