  The increasing applications of AI systems require personalized explanations
for their behaviors to various stakeholders since the stakeholders may have
various knowledge and backgrounds. In general, a conversation between
explainers and explainees not only allows explainers to obtain the explainees'
background, but also allows explainees to better understand the explanations.
In this paper, we propose an approach for an explainer to communicate
personalized explanations to an explainee through having consecutive
conversations with the explainee. We prove that the conversation terminates due
to the explainee's justification of the initial claim as long as there exists
an explanation for the initial claim that the explainee understands and the
explainer is aware of.
