Neuro-symbolic AI is an emerging subfield of Artificial Intelligence that brings together two
hitherto distinct approaches.” Neuro” refers to the artificial neural networks prominent in …

We introduce DeepProbLog, a probabilistic logic programming language that incorporates
deep learning by means of neural predicates. We show how existing inference and learning …

Complex event recognition (CER) applications exhibit various types of uncertainty, ranging
from incomplete and erroneous data streams to imperfect complex event patterns. We …

Neuro-symbolic and statistical relational artificial intelligence both integrate frameworks for
learning with logical reasoning. This survey identifies several parallels across seven …

An intelligent agent interacting with the real world will encounter individual people, courses,
test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of …

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

We introduce DeepProbLog, a neural probabilistic logic programming language that
incorporates deep learning by means of neural predicates. We show how existing inference …

While large language models (LLMs), such as GPT-3, appear to be robust and general, their
reasoning ability is not at a level to compete with the best models trained for specific natural …

Human reasoning can be understood as a cooperation between the intuitive, associative"
System-1''and the deliberative, logical" System-2''. For existing System-1-like methods in …

Since its birth, the field of Probabilistic Logic Programming has seen a steady increase of
activity, with many proposals for languages and algorithms for inference and learning. This …