A "partial ordering" is a way to heuristically order a set of examples (partial orderings are a set where, for certain pairs of elements, one precedes the other). While these orderings may only be approximate, they can be useful for guiding a search towards better regions of the data. To illustrate the value of that technique, this paper presents iSNEAK, an incremental human-in-the-loop AI problem solver. iSNEAK uses partial orderings and feedback from humans to prune the space of options. Further, in experiments with a dozen software models of increasing size and complexity (with up to 10,000 variables), iSNEAK only asked a handful of questions to return human-acceptable solutions that outperformed the prior state-of-the-art. We propose the use of partial orderings and tools like iSNEAK to solve the information overload problem where human experts grow fatigued and make mistakes when they are asked too many questions. iSNEAK mitigates the information overload problem since it allows humans to explore complex problem spaces in far less time, with far less effort.

翻译：“偏序关系”是一种启发式排序示例集合的方法（偏序关系是一种集合，其中对于某些元素对，一个元素先于另一个元素）。虽然这些排序可能仅是近似，但它们可用于引导搜索朝向数据中更优的区域。为阐明该技术的价值，本文提出iSNEAK——一种增量式人机协同AI问题求解器。iSNEAK利用偏序关系和人类反馈来剪枝选项空间。此外，在针对规模和复杂度递增（变量数高达10,000个）的十余个软件模型实验中，iSNEAK仅通过少量提问即可返回优于先前最优方法的人类可接受解。我们建议采用偏序关系及iSNEAK等工具来解决信息过载问题——当人类专家被询问过多问题时会产生疲劳并导致决策失误。iSNEAK通过使人类能够以更少时间和精力探索复杂问题空间，从而有效缓解信息过载问题。