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 …

The study and understanding of human behaviour is relevant to computer science, artificial
intelligence, neural computation, cognitive science, philosophy, psychology, and several …

Research on integrated neural-symbolic systems has made significant progress in the
recent past. In particular the understanding of ways to deal with symbolic knowledge within …

Covering the authors' own state-of-the-art research results, this book presents a rigorous,
modern account of the mathematical methods and tools required for the semantic analysis of …

Access control is a central problem in confidentiality management, in particular in the
healthcare domain, where many stakeholders require access to patients' health records …

The combination of learning and reasoning is an essential and challenging topic in neuro-
symbolic research. Differentiable inductive logic programming is a technique for learning a …

Artificial neural networks can be trained to perform excellently in many application areas.
Whilst they can learn from raw data to solve sophisticated recognition and analysis …

We present a fully connectionist system for the learning of first-order logic programs and the
generation of corresponding models: Given a program and a set of training examples, we …

We show that the conventional first-order algorithm of unification can be simulated by finite
artificial neural networks with one layer of neurons. In these unification neural networks, the …

Abstract Knowledge based artificial networks networks have been applied quite successfully
to propositional knowledge representation and reasoning tasks. However, as soon as these …