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 …

The current hype of Artificial Intelligence (AI) mostly refers to the success of machine
learning and its sub-domain of deep learning. However, AI is also about other areas, such …

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

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 …

Although non-verbal cues such as arm movement and eye gaze can convey robot intention,
they alone may not provide enough information for a human to fully understand a robot's …

AI agents support high stakes decision-making processes from driving cars to prescribing
drugs, making it increasingly important for human users to understand their behavior. Policy …

With advances in reinforcement learning (RL), agents are now being developed in high-
stakes application domains such as healthcare and transportation. Explaining the behavior …

Explainable AI techniques that describe agent reward functions can enhance human-robot
collaboration in a variety of settings. However, in order to effectively explain reward …

This paper investigates the use of model-based reinforcement learning in the context of ad
hoc teamwork. We introduce a novel approach, named TEAMSTER, where we propose …

As robots become increasingly prevalent in our communities, aligning the values motivating
their behavior with human values is critical. However, it is often difficult or impossible for …