Makoto Yokoo
Papers
3
Total Citations
318
H-Index
3
About
Makoto Yokoo is a pioneering researcher in the field of multiagent systems and distributed artificial intelligence, with particular expertise in distributed constraint optimization problems (DCOPs) and autonomous agent coordination. His work has significantly advanced the theoretical and practical foundations of how multiple intelligent agents can collaborate to solve complex, real-world problems. Yokoo's most influential contributions center on the DCOP framework, which provides mathematical and algorithmic tools for enabling distributed agents to coordinate toward shared goals. His research has pushed this field beyond purely theoretical boundaries, tackling the practical challenges of deploying DCOP algorithms in real-world environments such as mobile sensor networks, where agents must navigate unknown reward matrices and adapt under uncertainty. His 2007 conference proceedings work has garnered over 240 citations, reflecting the broad impact of his foundational contributions to autonomous agent research. A recurring theme in Yokoo's scholarship is the tension between exploration and exploitation in distributed online optimization — a challenge highly relevant to dynamic, uncertain environments. His work equips researchers and practitioners with principled approaches to balancing these competing demands. Through sustained contributions to multiagent coordination, Yokoo has helped shape modern intelligent systems design, earning him a respected place in the global AI research community.
Research Focus
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Top Papers
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