In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if
harnessed appropriately, may deliver the best of expectations over many application sectors …

In the meantime, a wide variety of terminologies, motivations, approaches, and evaluation
criteria have been developed within the research field of explainable artificial intelligence …

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

Neuro-Symbolic Artificial Intelligence–the combination of symbolic methods with methods
that are based on artificial neural networks–has a long-standing history. In this article, we …

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 …

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

This paper develops a novel methodology for using symbolic knowledge in deep learning.
From first principles, we derive a semantic loss function that bridges between neural output …

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

In the history of the quest for human-level artificial intelligence, a number of rival paradigms
have vied for supremacy. Symbolic artificial intelligence was dominant for much of the 20th …

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