  Human explanations are often contrastive, meaning that they do not answer the
indeterminate "Why?" question, but instead "Why P, rather than Q?".
Automatically generating contrastive explanations is challenging because the
contrastive event (Q) represents the expectation of a user in contrast to what
happened. We present an approach that predicts a potential contrastive event in
situations where a user asks for an explanation in the context of rule-based
systems. Our approach analyzes a situation that needs to be explained and then
selects the most likely rule a user may have expected instead of what the user
has observed. This contrastive event is then used to create a contrastive
explanation that is presented to the user. We have implemented the approach as
a plugin for a home automation system and demonstrate its feasibility in four
test scenarios.
