Editorial: Statistical Relational Artificial Intelligence

Fabrizio Riguzzi, Kristian Kersting, Marco Lippi, Sriraam Natarajan

Abstract: section{Introduction}Statistical Relational Artificial Intelligence (StarAI) aims at integrating logical (or relational) AI with probabilistic (or statistical) AI~\citep{deraedt2016,riguzzi18}. Relational AI achieved impressive results in structured machine learning and data mining, especially in bio- and chemo-informatics. Statistical AI is based on probabilistic (graphical) models that enable efficient reasoning and learning, and that have been applied to a wide variety of fields such as diagnosis, network communication, computational biology, computer vision, and robotics. Ultimately, StarAI may provide good starting points for developing \textit{Systems AI} ----the computational and mathematical modeling of complex AI systems--- and in turn an engineering discipline for Artificial Intelligence and Machine Learning.This Research Topic `Statistical Relational Artificial Intelligence' aims at presenting an overview of the latest approaches in StarAI. This topic was followed by a summer school\footnote{\url{http://acai2018.unife.it/}} held in 2018 in Ferrara, Italy, as part of the series of Advanced Courses on AI (ACAI) promoted by the European Association for Artificial Intelligence.\section*{Papers Included in this Research Topic}Previous issues on similar topics in other journals and books mainly focused on modeling, learning purely from data and/or lifted inference. This issue went further and addressed more novel and pertinent topics such as grammatical inference, hu...