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About

Dana Nau is a leading figure in artificial intelligence, with foundational contributions to automated planning, robotics, and manufacturing. His research bridges the gap between high-level symbolic reasoning and low-level physical action, particularly through the development of Hierarchical Task Network (HTN) planning—a paradigm that has become a cornerstone of practical AI. In his highly cited work, *Artificial Intelligence Applications in Manufacturing* (1992, 50 citations), Nau explored how machine learning and geometric reasoning could optimize industrial planning and decision-making, influencing generations of researchers in manufacturing automation. He has also advanced real-time robotic coverage planning, as seen in his 2011 paper (19 citations), which developed strategies for autonomously exploring unknown environments—a critical capability for search-and-rescue and planetary rovers. More recently, Nau has focused on integrating hierarchical goal networks with motion planners to enable seamless human-robot collaboration in assembly cells. His 2025 book, *Acting, Planning, and Learning*, synthesizes decades of work into a vision for intelligent agents that can reason, act, and adapt in dynamic worlds. With a career spanning over three decades, Nau’s research continues to shape how autonomous systems plan and execute complex tasks in the real world.