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One of the most remarkable things about the human moral mind is itsflexibility. We can make moral judgments about cases we have never seen before.We can decide that pre-established rules should be broken. We can invent novelrules on the fly. Capturing this flexibility is one of the central challengesin developing AI systems that can interpret and produce human-like moraljudgment. This paper details the results of a study of real-world decisionmakers who judge whether it is acceptable to break a well-established norm:``no cutting in line.'' We gather data on how human participants judge theacceptability of line-cutting in a range of scenarios. Then, in order toeffectively embed these reasoning capabilities into a machine, we propose amethod for modeling them using a preference-based structure, which captures anovel modification to standard ``dual process'' theories of moral judgment.

When Is It Acceptable to Break the Rules? Knowledge Representation of Moral Judgement Based on Empirical Data