Masaki Ban

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About

Masaki Ban is a researcher in artificial intelligence and multi-agent systems, with a primary focus on robotic soccer simulation. His work centers on developing advanced decision-making frameworks for autonomous agents in dynamic, real-time environments. Ban’s most notable contribution is his analysis of Expected Possession Value (EPV) in the RoboCup Soccer Simulation 2D (RSS2D) domain, a platform where 11 autonomous robots per team compete in a virtual soccer match. By adapting EPV—a metric originally used in human sports analytics—to the RSS2D setting, Ban provides agents with a sophisticated tool to evaluate the potential value of ball possession in constantly shifting game states. This approach enables more strategic, long-term planning rather than reactive play. While his 2023 paper has garnered initial citations, his work represents a significant step toward bridging sports analytics and multi-agent reinforcement learning. Ban’s research is particularly valuable for students and researchers interested in applying data-driven evaluation metrics to improve coordination and decision-making in autonomous robotic teams, with potential implications beyond soccer into broader multi-agent coordination tasks.