Abstract:

In this lecture, we will journey through the core principles of Al agents, building a conceptual bridge from foundational theories to cutting-edge practical implementations. Attendees will gain insights into how autonomous agents operate, starting with basic Al agent architectures and evolving into sophisticated web automation systems. Highlighting our latest research with WebPilot, the lecture will showcase how integrating Monte Carlo Tree Search with a dual optimization strategy addresses the complexities of dynamic web tasks-mitigating vast action spaces and uncertainty through strategic exploration and adaptive decision-making.

Bio:

Dr. Yunpu Ma is a Postdoc at the University of Munich, working with Prof. Volker Tresp and Prof. Thomas Seidl on multimodal foundation models and dynamic graphs.

Additionally, he is a research scientist at Siemens, specializing in quantum machine learning. Before Siemens, he spent three years as an Al researcher at LMU, where he earned his Ph.D., focusing on temporal knowledge graphs.

His research interests encompass structured data learning, multimodal foundation models, and quantum machine learning. His ultimate research goal is to advance general Al.