One of the main challenges in materials discovery is efficiently exploring the vast search
space for targeted properties as approaches that rely on trial-and-error are impractical. We …

Reinforcement learning is a branch of machine learning concerned with using experience
gained through interacting with the world and evaluative feedback to improve a system's …

A broad introduction to algorithms for decision making under uncertainty, introducing the
underlying mathematical problem formulations and the algorithms for solving them …

Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …

Artificial neural networks, deep-learning methods and the backpropagation algorithm form
the foundation of modern machine learning and artificial intelligence. These methods are …

Reinforcement learning offers to robotics a framework and set of tools for the design of
sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic …

In recent years, the interest in leveraging quantum effects for enhancing machine learning
tasks has significantly increased. Many algorithms speeding up supervised and …

The study of multi-agent systems (MAS) focuses on systems in which many intelligent agents
interact with each other. These agents are considered to be autonomous entities such as …

The significantly expanded and updated new edition of a widely used text on reinforcement
learning, one of the most active research areas in artificial intelligence. Reinforcement …

In the feature subset selection problem, a learning algorithm is faced with the problem of
selecting a relevant subset of features upon which to focus its attention, while ignoring the …