Rough Sets were introduced by Zdzisław Pawlak in the early ’80s and developed further as a method of knowledge representation and processing based on uncertain data and incomplete information. Nowadays, Rough Set theory is widely recognized to have great importance in several fields, including artificial intelligence, mathematics, knowledge representation and machine learning, as evidenced by the increasing number of works concerned with its applications and theoretical foundations.

The Report: 1. Anniversary Panel Session: Rough Sets Turn 40: Uncertainty Management Perspective 2. Keynote Talk by Andrzej Skowron: Perceptual Rough Set Approach in Interactive Granular Computing

The Report: 1. Anniversary Panel Session: (The Next) 40 Years of Rough Sets 2. PP-RAI Contest for the Most Influential Article on Rough Sets co-authored by Polish Researchers in 2020-2021