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About

Ah-Hwee Tan is a leading researcher in cognitive robotics and multi-agent systems, whose work bridges artificial intelligence with biologically inspired models. His primary research areas include deep reinforcement learning for multi-robot coordination, brain-inspired cognitive architectures, and interactive teachable agents. Tan’s most impactful contribution is his 2019 paper on end-to-end deep reinforcement learning for multi-agent collaborative exploration (25 citations), which addresses the critical challenge of interference among multiple robots during unknown environment exploration. He introduced the CNN-based Multi model, significantly advancing autonomous robotic coordination. Tan also developed the FALCON-X cognitive agent model (2010), integrating ACT-R architecture with fusion Adaptive Resonance Theory networks to combine declarative knowledge and reinforcement learning. His work on self-aware sociable agents (2017) explores how conversational robots can develop identity and personal experiences for better human interaction. Additionally, Tan’s eHealthPortal project (2017) applies his multi-agent frameworks to healthcare challenges for aging populations. Through his interactive teachable cognitive agents framework (2016), he has created smart building blocks for developing complex multi-agent systems. Tan’s research consistently pushes the boundaries of how autonomous agents can learn, collaborate, and interact with humans in natural, intelligent ways.