Yuki Yamano
Papers
2
Total Citations
5
H-Index
2
About
Yuki Yamano is a researcher whose work lies at the intersection of swarm intelligence, reinforcement learning, and neuro-fuzzy systems. Their research focuses on designing adaptive, cooperative behaviors for multi-agent systems by equipping individual agents with higher cognitive functions, such as pattern recognition and online learning. Yamano’s major contributions include the development of a neuro-fuzzy reinforcement learning framework that enables swarm agents to acquire sophisticated, cooperative behaviors without requiring pre-programmed rules. This approach bridges the gap between simple, reactive swarm models and more intelligent, adaptive systems. Among their notable works, "Adaptive Swarm Behavior Acquisition Using a Neuro-Fuzzy Reinforcement Learning System" (2013) and "A Neuro-fuzzy Network with Reinforcement Learning Algorithms for Swarm Learning" (2012) have garnered early citations, laying the groundwork for future advances in autonomous, learning-based swarms. By integrating reinforcement learning with fuzzy logic and neural networks, Yamano has opened new pathways for creating resilient, self-organizing robotic teams and distributed AI systems. Their research is particularly relevant to students and researchers interested in bio-inspired robotics, adaptive control, and the future of decentralized intelligence.
Research Focus
Key Achievements
Top Papers
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- 2