Abstract Decision Trees (DTs) are predictive models in supervised learning, known not only
for their unquestionable utility in a wide range of applications but also for their interpretability …

Artificial intelligence (AI) and especially reinforcement learning (RL) have the potential to
enable agents to learn and perform tasks autonomously with superhuman performance …

Interpretability in machine learning (ML) is crucial for high stakes decisions and
troubleshooting. In this work, we provide fundamental principles for interpretable ML, 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 …

Recent advances in machine learning have led to growing interest in Explainable AI (xAI) to
enable humans to gain insight into the decision-making of machine learning models …

Intelligent agents must be able to communicate intentions and explain their decision-making
processes to build trust, foster confidence, and improve human-agent team dynamics …

Explainable reinforcement learning (XRL) is an emerging subfield of explainable machine
learning that has attracted considerable attention in recent years. The goal of XRL is to …

Decision trees (DTs) epitomize what have become to be known as interpretable machine
learning (ML) models. This is informally motivated by paths in DTs being often much smaller …

Reinforcement Learning (RL) systems achieved outstanding performance in different
domains such as Atari games, finance, healthcare, and self-driving cars. However, their …

Despite recent success of deep learning models in research settings, their application in
sensitive domains remains limited because of their opaque decision-making processes …