Semantic Image Interpretation (SII) is the task of extracting structured semantic descriptions
from images. It is widely agreed that the combined use of visual data and background …

A multitude of different probabilistic programming languages exists today, all extending a
traditional programming language with primitives to support modeling of complex, structured …

We study the problem of combining neural networks with symbolic reasoning. Recently
introduced frameworks for Probabilistic Neurosymbolic Learning (PNL), such as …

Probabilistic Logic Programming extends Logic Programming by enabling the
representation of uncertain information by means of probability theory. Probabilistic Logic …

Weighted model counting has recently been extended to weighted model integration, which
can be used to solve hybrid probabilistic reasoning problems. Such problems involve both …

Today, there exist many different probabilistic programming languages as well as more
inference mechanisms for these languages. Still, most logic programming-based languages …

The combination of logic programming and probability has proven useful for modeling
domains with complex and uncertain relationships among elements. Many probabilistic logic …

Based on a weighted knowledge graph to represent first-order knowledge and combining it
with a probabilistic model, we propose a methodology for creating a medical knowledge …

Modeling problems containing a mixture of Boolean and numerical variables is a long-
standing interest of Artificial Intelligence. However, performing inference and learning in …

This paper presents a revision of Real Logic and its implementation with Logic Tensor
Networks and its application to Semantic Image Interpretation. Real Logic is a framework …