Designing good models is one of the main challenges for obtaining realistic and useful decision support and optimization systems. Traditionally combinatorial models are crafted by interacting with domain experts with limited accuracy guarantees. Nowadays we have access to data sets of unprecedented scale and accuracy about the systems we are deciding on.
In this talk, we propose a methodology called Empirical Model Learning that uses machine learning to extract data-driven decision model components and integrates them into an expert-designed decision model. We outline the main domains where EML could be useful and we show how to ground Empirical Model Learning on some applications.
To participate in the seminar #frAIday, please register here. We will send you a link to the Zoom event.
Michela Milano, Professor of Artificial Intelligence at DISI – University of Bologna; Head of Centro Interdipartimentale Alma Mater Research Institute for Human-Centered Artificial Intelligence — (Alma AI).