The deployment of systems of artificial intelligence (AI) in high-risk settings warrants the
need for trustworthy AI. This crucial requirement is highlighted by recent EU guidelines and …

The last decade witnessed an ever-increasing stream of successes in Machine Learning
(ML). These successes offer clear evidence that ML is bound to become pervasive in a wide …

Compilation into propositional languages finds a growing number of practical uses,
including in constraint programming, diagnosis and machine learning (ML), among others …

Recent work has shown that not only decision trees (DTs) may not be interpretable but also
proposed a polynomial-time algorithm for computing one PI-explanation of a DT. This paper …

Explanation is necessary for humans to understand and accept decisions made by an AI
system when the system's goal is known. It is even more important when the AI system …

Abductive reasoning generates explanatory hypotheses for new observations using prior
knowledge. This paper investigates the use of forgetting, also known as uniform …

Researchers focusing on how artificial intelligence (AI) methods explain their decisions often
discuss controversies and limitations. Some even assert that most publications offer little to …

Knowledge compilation (KC) languages find a growing number of practical uses, including
in Constraint Programming (CP) and in Machine Learning (ML). In most applications, one …

In recent years, the impact of machine learning (ML) and artificial intelligence (AI) in society
has been absolutely remarkable. This impact is expected to continue in the foreseeable …

As a field of AI, Machine Reasoning (MR) uses largely symbolic means to formalize and
emulate abstract reasoning. Studies in early MR have notably started inquiries into …