The use of Deep Reinforcement Learning (DRL) schemes has increased dramatically since
their first introduction in 2015. Though uses in many different applications are being found …

Reinforcement Learning (RL) is a popular machine learning paradigm where intelligent
agents interact with the environment to fulfill a long-term goal. Driven by the resurgence of …

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

Broad-XAI moves away from interpreting individual decisions based on a single datum and
aims to provide integrated explanations from multiple machine learning algorithms into a …

Learning controllers with offline data in decision-making systems is an essential area of
research due to its potential to reduce the risk of applications in real-world systems …

Artificial Intelligence (AI) systems have made remarkable progress, attaining super-human
performance across various domains. This presents us with an opportunity to further human …

Multi-agent reinforcement learning (MARL) is a widely used Artificial Intelligence (AI)
technique. However, current studies and applications need to address its scalability, non …

Deep neural networks (DNNs) are increasingly being used as controllers in reactive
systems. However, DNNs are highly opaque, which renders it difficult to explain and justify …

The digitisation of agriculture, integral to Agriculture 4.0, has brought significant benefits
while simultaneously escalating cybersecurity risks. With the rapid adoption of smart farming …

While AI algorithms have shown remarkable success in various fields, their lack of
transparency hinders their application to real-life tasks. Although explanations targeted at …