Editorial: Statistical Relational Artificial Intelligence

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Extending the data dictionary for data/knowledge management

Current relational database technology provides the means for efficiently storing and retrieving large amounts of data. By combining techniques learned from the field of artificial intelligence with this technology, it is possible to expand the capabilities of such systems. This paper suggests using the expanded domain concept, an object-oriented organization, and the storing of knowledge rules within the relational database as a solution to the unique problems associated with CAD/CAM and engineering data

"Let us make ROBOT in our image, according to our likeness" : an examination of robots in several science fiction films through the christian concept of the "image of God"

The paper examines representations of robots in several films: Bicentennial Man (1999), Star Trek: Nemesis (2002) and Chappie (2015) in the light of the Christian concept of imago Dei. According to Victoria Nelson, in the last 50 years artificial intelligence in pop-culture works has frequently been presented as holiness. Her interpretation can be linked with the outcome of research of scholars, who revealed that the Euro-American view on technology is deeply rooted in Christian thought. The author’s main line of argument is embedded in Noreen Herzfeld’s observation, which demonstrated the striking similarities between the relational approach to research into artificial intelligence and the relational interpretation of the notion of imago Dei by Karl Barth. Herzfeld suggests that the robots in the examined films can be viewed through a relational approach to the concept of imago Dei, which entails a relational definition of intelligence

From Statistical Relational to Neurosymbolic Artificial Intelligence: a Survey

This survey explores the integration of learning and reasoning in two different fields of artificial intelligence: neurosymbolic and statistical relational artificial intelligence. Neurosymbolic artificial intelligence (NeSy) studies the integration of symbolic reasoning and neural networks, while statistical relational artificial intelligence (StarAI) focuses on integrating logic with probabilistic graphical models. This survey identifies seven shared dimensions between these two subfields of AI. These dimensions can be used to characterize different NeSy and StarAI systems. They are concerned with (1) the approach to logical inference, whether model or proof-based; (2) the syntax of the used logical theories; (3) the logical semantics of the systems and their extensions to facilitate learning; (4) the scope of learning, encompassing either parameter or structure learning; (5) the presence of symbolic and subsymbolic representations; (6) the degree to which systems capture the original logic, probabilistic, and neural paradigms; and (7) the classes of learning tasks the systems are applied to. By positioning various NeSy and StarAI systems along these dimensions and pointing out similarities and differences between them, this survey contributes fundamental concepts for understanding the integration of learning and reasoning.Comment: To appear in Artificial Intelligence. Shorter version at IJCAI 2020 survey track, https://www.ijcai.org/proceedings/2020/0688.pd