Autonomous driving has achieved significant milestones in research and development over
the last two decades. There is increasing interest in the field as the deployment of …

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 …

As more non-AI experts use complex AI systems for daily tasks, there has been an
increasing effort to develop methods that produce explanations of AI decision making that …

Although deep reinforcement learning has become a promising machine learning approach
for sequential decision-making problems, it is still not mature enough for high-stake domains …

Alignment of human and autonomous agent values and objectives is vital in human-
autonomy teaming settings which require collaborative action toward a common goal. In …

When users receive either a positive or negative outcome from an automated system,
Explainable AI (XAI) has almost exclusively focused on how to mutate negative outcomes …

Part-prototype networks (eg, ProtoPNet, ProtoTree, and ProtoPool) have attracted broad
research interest for their intrinsic interpretability and comparable accuracy to non …

Diffusion-based planning has shown promising results in long-horizon, sparse-reward tasks
by training trajectory diffusion models and conditioning the sampled trajectories using …

Energy storage systems (ESS) are pivotal component in the energy market, serving as both
energy suppliers and consumers. ESS operators can reap benefits from energy arbitrage by …

Prototypical-part interpretable methods, eg, ProtoPNet, enhance interpretability by
connecting classification predictions to class-specific training prototypes, thereby offering an …