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

Equipping machines with comprehensive knowledge of the world's entities and their
relationships has been a longstanding goal of AI. Over the last decade, large-scale …

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 …

We present a survey of ways in which existing scientific knowledge are included when
constructing models with neural networks. The inclusion of domain-knowledge is of special …

Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce
a hypothesis (a set of logical rules) that generalises training examples. As ILP turns 30, we …

Optimisation of tissue engineering (TE) processes requires models that can identify
relationships between the parameters to be optimised and predict structural and …

We describe an inductive logic programming (ILP) approach called learning from failures. In
this approach, an ILP system (the learner) decomposes the learning problem into three …

Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to
induce a hypothesis (a logic program) that generalises given training examples and …

Intrusion detection systems (IDS) are commonly categorized into misuse based, anomaly
based and specification based IDS. Both misuse based IDS and anomaly based IDS are …

Both logic programming in general and Prolog in particular have a long and fascinating
history, intermingled with that of many disciplines they inherited from or catalyzed. A large …