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Sugawara Lab
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The lab studies multi-agent systems, in which autonomous programs cooperate and learn from one another.
AboutAbout the lab
Programs that cooperate and learn from one another
The Sugawara Lab studies autonomous agents and multi-agent systems, a field within artificial intelligence.
The lab builds mechanisms by which many programs (agents) connected over the Internet learn autonomously while cooperating and coordinating with one another.
Beyond the learning of individual agents, the lab also works on theory and simulation-based validation aimed at improving the efficiency of the system as a whole.
Key terms
- multi-agent system
- A system in which many programs (agents), each acting on its own judgment, work together cooperatively
- autonomous agent
- A program that assesses situations and acts on its own, without receiving instructions from a person at every step
- reinforcement learning
- A learning method in which an agent discovers better actions on its own, based on the rewards it receives as a result of its actions
NewsLab news
View More ↗ResearchResearch themes
The Sugawara Lab studies autonomous agents and multi-agent systems, a branch of artificial intelligence (AI) in which intelligent programs connected over the Internet cooperate and coordinate with one another. Its work spans theoretical analysis and simulation-based validation through to real-world applications.
*Illustrative imageMachine Learning and Planning
The lab studies algorithms that let each agent learn through reinforcement learning or statistical and probabilistic learning, understand its own role, and choose actions that benefit the system as a whole.
*Illustrative imageProtocols for Cooperation, Coordination, and Competition
The lab proposes and develops methods for improving overall efficiency while multiple agents cooperate and compete.
*Illustrative imageInteraction among Large Numbers of Agents
In large, complex environments, the lab studies mechanisms by which agents autonomously cooperate and integrate with one another to raise overall efficiency, with the agents themselves learning those mechanisms.
*Illustrative imageOrganization (Agent Networks)
Using theory and simulation, the lab studies mechanisms that appropriately place many agents with different roles and capabilities and divide work among them as the environment changes.
MembersFaculty
- ProfessorToshiharu Sugawara
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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: “Ant robot” Tiia Monto / CC BY 4.0 (cropped)
- Machine Learning and Planning: “Persian Backgammon board made in Khatam technique” Nikos Kitsakis / CC BY 4.0 (cropped)
- Protocols for Cooperation, Coordination, and Competition: “Moray cleaner wrasse komodo” Nhobgood Nick Hobgood / CC BY-SA 3.0 (cropped)
- Interaction among Large Numbers of Agents: “Murmuration 11-2025” Skander zarrad / CC BY-SA 4.0 (cropped)
- Organization (Agent Networks): “Abandoned bee honeycomb” Charles J. Sharp / CC BY-SA 4.0 (cropped)
- Related lab (Ishikawa Lab): “Banana Plant Flask by Max Gruber” Max Gruber / CC BY 4.0 (cropped)
- Related lab (Nakajima Lab): “Augmented Reality Headset 02” C.Suthorn / CC BY-SA 4.0 (cropped)
- Related lab (Kasahara Lab): “Labyrinthine circuit board lines - Flickr - quapan” quapan / CC BY 2.0 (cropped)
- Related lab (Uchida Lab): “Network Visualization” Martin Grandjean / CC BY-SA 4.0 (cropped)
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