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 …

The explainability of Deep Neural Networks (DNNs) has recently gained significant
importance especially in safety-critical applications such as automated/autonomous …

While reinforcement learning (RL) algorithms have been successfully applied to numerous
tasks, their reliance on neural networks makes their behavior difficult to understand and …

Deep Reinforcement Learning (DRL) has demonstrated promising capability in solving
complex control problems. However, DRL applications in safety-critical systems are …

To effectively track resident space objects (RSOs), the tasking of sensors in a distributed
network requires flexible and adaptive approaches. While Deep Reinforcement Learning …

While Deep Reinforcement Learning (DRL) has emerged as a promising solution for
intricate control tasks, the lack of explainability of the learned policies impedes its uptake in …

Nowadays, large-scale foundation models are being increasingly integrated into numerous
safety-critical applications, including human-autonomy teaming (HAT) within transportation …

In this paper, we demonstrate the feasibility of alterfactual explanations for black box image
classifiers. Traditional explanation mechanisms from the field of Counterfactual Thinking are …

For the purpose of understanding the impact of a Reinforcement Learning (RL) agent's
decisions on the satisfaction of a given arbitrary predicate, we present a method based on …

Understanding how failure occurs and how it can be prevented in reinforcement learning
(RL) is necessary to enable debugging, maintain user trust, and develop personalized …