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.

翻译：人类解释通常是对比性的，即它们不回答不确定的“为什么？”问题，而是回答“为什么是P，而不是Q？”自动生成对比性解释具有挑战性，因为对比性事件（Q）代表用户与实际情况相反的期望。我们提出了一种方法，在用户询问基于规则系统背景下需要解释的情境中，预测可能的对比性事件。我们的方法分析需要解释的情况，然后选择用户最可能期望的规则，而非用户观察到的规则。这一对比性事件随后被用于生成对比性解释，并将其呈现给用户。我们已将该方法实现为家庭自动化系统的插件，并在四个测试场景中验证了其可行性。