Abstract Explainability in Artificial Intelligence (AI) has been revived as a topic of active
research by the need of conveying safety and trust to users in the “how” and “why” of …

Despite its great success, machine learning can have its limits when dealing with insufficient
training data. A potential solution is the additional integration of prior knowledge into the …

In this paper, we present a multiple knowledge representation (MKR) framework and discuss
its potential for develo** big data artificial intelligence (AI) techniques with possible …

Abstract Current advances in Artificial Intelligence (AI) and Machine Learning have achieved
unprecedented impact across research communities and industry. Nevertheless, concerns …

Attempts at combining logic and neural networks into neurosymbolic approaches have been
on the increase in recent years. In a neurosymbolic system, symbolic knowledge assists …

There is a dilemma regarding the accuracy and reality of vehicle trajectory prediction.
Balancing and predicting the effective trajectory is a topic of debate in autonomous driving …

Current advances in Artificial Intelligence and machine learning in general, and deep
learning in particular have reached unprecedented impact not only across research …

Artificial Neural Networks are powerful function approximators capable of modelling
solutions to a wide variety of problems, both supervised and unsupervised. As their size and …

Although deep reinforcement learning has become a promising machine learning approach
for sequential decision-making problems, it is still not mature enough for high-stake domains …

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