14.30
Welcome
Willem Adriaan De Graaf (PhD Coordinator)

14.45
Guido Sciavicco (Department of Mathematics and Informatic, Università degli Studi di Ferrrara)
Modal Symbolic Learning
Abstract: Modal Symbolic Learning is the subfield of Symbolic Learning that deals with more-than-propositional logic, specifically with modal logic, and Symbolic Learning is the subfield of Artificial Intelligence that focuses on machine learning of logical models from data, for example for classification. While it is well-known that symbolic models can be learnt from tabular data, symbolic models for non-tabular data have been mainly neglected. Modal Symbolic Learning fills in this gap by using modal logic (and, specifically, temporal and spatial logic) to learn from non-tabular data (and, specifically, temporal and spatial data). Modal Symbolic Learning has been successfully applied to real-world temporal and spatial situations. In this talk, we shall briefly review its theoretical foundations and some of the most basic results that make Modal Symbolic Learning possible.

15.45
Small Break

16.00
XL cycle PhD Students Presentation