Towards Transparent Robotic Planning via Contrastive Explanations
Shenghui Chen,Kayla Boggess,Lu Feng,Shenghui Chen,Kayla Boggess,Lu Feng
Providing explanations of chosen robotic actions can help to increase the transparency of robotic planning and improve users' trust. Social sciences suggest that the best explanations are contrastive, explaining not just why one action is taken, but why one action is taken instead of another. We formalize the notion of contrastive explanations for robotic planning policies based on Markov decision...
