Artificial Intelligence (AI) has emerged as a complementary technology in supply chain
research. However, the majority of AI approaches explored in this context afford little to no …

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 …

Deep Learning (DL) models proved themselves to perform extremely well on a wide variety
of learning tasks, as they can learn useful patterns from large data sets. However, purely …

In recent years, there has been an increasing interest in exploiting logically specified
background knowledge in order to obtain neural models (i) with a better performance,(ii) …

The unification of statistical (data-driven) and symbolic (knowledge-driven) methods is
widely recognized as one of the key challenges of modern AI. Recent years have seen a …

The more intelligent systems based on sub-symbolic techniques pervade our everyday lives,
the less human can understand them. This is why symbolic approaches are getting more …

Abstract Although Deep Neural Networks (DNNs) have great generalization and prediction
capabilities, their functioning does not allow a detailed explanation of their behavior …

Neural-symbolic computing (NeSy), which pursues the integration of the symbolic and
statistical paradigms of cognition, has been an active research area of Artificial Intelligence …

Deep learning methods are highly accurate, yet their opaque decision process prevents
them from earning full human trust. Concept-based models aim to address this issue by …