Papers
1
Total Citations
10
H-Index
1
About
Harada Maya is a researcher in multi-agent systems and reinforcement learning, with a focus on security and autonomous robotics. Her most cited work, "Cooperative capture by multi-agent using reinforcement learning application for security patrol systems" (2015, 10 citations), addresses the challenge of coordinating multiple autonomous robots to enclose and capture an intruder within a building. This study applies reinforcement learning to develop optimal pursuit strategies, contributing to the broader field of multi-agent pursuit-evasion problems. Harada’s research integrates artificial intelligence, robotics, and security systems, aiming to enhance automated patrol and threat response. Her work demonstrates practical applications of reinforcement learning in real-world security scenarios, offering a foundation for future advancements in cooperative multi-agent systems. With a focus on intelligent, adaptive algorithms, Harada Maya’s contributions support the development of more efficient and autonomous security patrol systems, highlighting the potential of AI-driven robotics in critical infrastructure protection.
Research Focus
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Top Papers
- 1