Reinforcement learning with Takagi-Sugeno-Kang fuzzy systems

Dr. Barnabas Bede
DigiPen Institute of Technology, Redmond, USA.
Personal Summary:
Dr. Barnabas Bede earned his Ph.D. in Mathematics from Babes-Bolyai University of Cluj-Napoca, Romania. His research interests include Machine Learning, Fuzzy Sets and Fuzzy Logic, and Modeling under Uncertainty. He is a Professor of Mathematics at DigiPen Institute of Technology in Redmond, WA, USA, and he serves as Program Director of the Bachelor of Science in Computer Science in Machine Learning. Before that, he held positions at the University of Rio Grande Valley, Texas, the University of Texas at El Paso, the University of Oradea Romania, and Óbuda University in Hungary. He has published more than 100 research publications, including three research monographs.
Talk Title:
Reinforcement learning with Takagi-Sugeno-Kang fuzzy systems
Talk Abstract:
In this talk, we will explore the construction of novel fuzzy-based explainable machine learning algorithms and training of such models using reinforcement learning. Fuzzy systems are widely used in modeling uncertainty and are based on fuzzy rules describing connections between various variables in this setting. We will start by studying the equivalence between a layer of a Neural Network with ReLU activation and a Takagi-Sugeno (TS) fuzzy system with triangular membership function.
We will discuss applications of the interpretable Machine Learning algorithms introduced here to physics engines used in video games. We will also explore the equivalence between neural networks with multiple layers and multiple inputs with ReLU activation, and Takagi-Sugeno systems with similar multi-layer structure. This method allows us to translate a Neural Network into a Fuzzy Systems, potentially extracting fuzzy rules that can make neural networks more interpretable. Future research directions are also explored.