Stefan Kramer, Professor of Computer Science, Gutenberg University Mainz, Germany

Title: 35 Years of Scientific Discovery: Computational Explorations of the Creative Processes': From the Early Days to the State of the Art

Abstract: It was 35 years ago, in April 1987, when the first book on computational models of scientific discovery was published: "Scientific Discovery: Computational Explorations of the Creative Processes" by Pat Langley, Herbert Simon, Gary Bradshaw, and Jan Zytkow contained a comprehensive account of systems for discovering quantitive empirical laws as well the discovery of qualitative and structural models, and marked an important milestone in a new branch of AI. Since then, methods for equation discovery, symbolic regression and the automation of science have been developed and refined, with many interesting problems remaining. Currently, deep neural networks (DNNs), representation learning, explainable AI (XAI), graph neural networks (GNNs), and many other technical innovations are bringing new elements into the field. At the same time, progress in the natural and life sciences is increasingly made by (and often requires) methods from AI and ML to produce models with high predictive and explanatory power. In the talk, I will review progress in the field, applications from the natural and life sciences as well as a new test environment, with many options for extensions, that frames machine discovery as a reinforcement learning problem.