The combination of logic and probability proved very useful for modeling domains with complex and uncertain relationships among entities. Machine learning approaches based on such combinations have recently achieved important results, originating the fields of Statistical Relational Learning, Probabilistic Inductive Logic Programming and, more generally, Statistical Relational Artificial Intelligence. This tutorial will briefly introduce probabilistic logic programming and probabilistic description logics and overview the main systems for learning these formalisms both in terms of parameters and of structure. The tutorial includes a significant hands-on experience with the systems ProbLog2, PITA, TRILL and SLIPCOVER using their online interfaces: https://dtai.cs.kuleuven.be/problog/, http://cplint.eu/ and http://trill-sw.eut/.
Bring your own laptop!