  As AI becomes an integral part of our lives, the development of explainable
AI, embodied in the decision-making process of an AI or robotic agent, becomes
imperative. For a robotic teammate, the ability to generate explanations to
justify its behavior is one of the key requirements of explainable agency.
Prior work on explanation generation has been focused on supporting the
rationale behind the robot's decision or behavior. These approaches, however,
fail to consider the mental demand for understanding the received explanation.
In other words, the human teammate is expected to understand an explanation no
matter how much information is presented. In this work, we argue that
explanations, especially those of a complex nature, should be made in an online
fashion during the execution, which helps spread out the information to be
explained and thus reduce the mental workload of humans in highly cognitive
demanding tasks. However, a challenge here is that the different parts of an
explanation may be dependent on each other, which must be taken into account
when generating online explanations. To this end, a general formulation of
online explanation generation is presented with three variations satisfying
different "online" properties. The new explanation generation methods are based
on a model reconciliation setting introduced in our prior work. We evaluated
our methods both with human subjects in a simulated rover domain, using NASA
Task Load Index (TLX), and synthetically with ten different problems across two
standard IPC domains. Results strongly suggest that our methods generate
explanations that are perceived as less cognitively demanding and much
preferred over the baselines and are computationally efficient.
