Extensive research into eXplainable AI (XAI) has raised interest in generating counterfactual
(CF) explanations. In the past, minimizing the perturbation of input was considered a priority …

Understanding emerging behaviors of reinforcement learning (RL) agents may be difficult
since such agents are often trained in complex environments using highly complex decision …

The black-box nature of deep reinforcement learning (RL) hinders them from real-world
applications. Therefore, interpreting and explaining RL agents have been active research …

While recent years have witnessed the emergence of various explainable methods in
machine learning, to what degree the explanations really represent the reasoning process …

Counterfactual explanations, which deal with “why not?” scenarios, can provide insightful
explanations to an AI agent's behavior [Miller [38]]. In this work, we focus on generating …

Abstract Machine learning plays a role in many deployed decision systems, often in ways
that are difficult or impossible to understand by human stakeholders. Explaining, in a human …

As an essential post-hoc explanatory method, counterfactual explanation enables people to
understand and react to machine learning models. Works on counterfactual explanation …

Causal explanations present an intuitive way to understand the course of events through
causal chains, and are widely accepted in cognitive science as the prominent model …

Counterfactual explanations focus on" actionable knowledge" to help end-users understand
how a machine learning outcome could be changed to a more desirable outcome. For this …

With the rapid development of deep reinforcement learning (DRL) techniques, there is an
increasing need to understand and interpret DRL policies. While recent research has …